infovis95--528697 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study: 3D displays of Internet traffic. The explosive growth in world-wide communications, especially the Internet, has highlighted the need for techniques to visualize network traffic. The traditional node and link network displays work well for small datasets but become visually cluttered and uninterpretable for large datasets. A natural 3D metaphor for displaying world-wide network data is to position the nodes on a globe and draw arcs between them coding the traffic. This technique has several advantages of over the traditional 2D displays, it naturally reduces line crossing clutter, provides an intuitive model for navigation and indication of time, and retains the geographic context. Coupling these strengths with some novel interaction techniques involving the globe surface translucency and arc heights illustrates the usefulness for this class of displays. Cox, K.C. Eick, S.G. case study geographic interaction navigation network InfoVis 3D displays Internet Internet traffic arc height computer animation data visualisation geographic context globe surface translucency large data sets line crossing clutter reduction navigation network traffic visualisation stereo image processing telecommunication computing telecommunication traffic time indicator world-wide communications world-wide network data 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528696 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study: visualizing Internet resources. The goal is to improve the ability of people from all walks of life and interests to access, search, and use the information distributed in Internet resources. The process of interacting with information resources starts with browsing, continues with digesting and assimilating pieces of information, terminates with generation of new information, and begins anew with analysis of pre-existing and new information. Our approach is user-centric-taking users needs into account by allowing them to interact with the information contained in large arrays of documents. The visualization process is an integral part of the overall process. We have covered three related categories in this methodology. The first one is browsing through the World-Wide Web (WWW) hyperspace without becoming lost, based on a visual representation of the hyperspace hierarchical structure (hyperspace view). The second category is overcoming the rigidity of the WWW by allowing the user to construct interactively and visually a personal hyperspace of information, linking the documents according to the application or problem domain, or to the user's own perception, experience, culture, or way of thinking. The third category includes discovery and analysis of new information and relationships in retrieved documents by aggregating relevant information and representing it visually. Croall, J. Gershon, N. LeVasseur, J. Pernicks, A. Ruh, W. Winstead, J. case study perception InfoVis Internet Internet resource visualisation World-Wide Web hyperspace browsing data visualisation document linking documents hyperspace hierarchical structure information analysis information resource interaction information retrieval new information analysis new information discovery personal information hyperspace retrieved documents user-centric approach visual representation 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528695 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study: an empirical investigation of thumbnail imag recognition. The use of thumbnails (i.e., miniatures) in the user-interface of image databases allows searching and selection of images without the need for naming policies. Treating parent images prior to reduction with edge-detecting smoothing, lossy image compression, or static codebook compression resulted in thumbnails where the distortion caused by reduction was lessened. An experiment assessing these techniques found resulting thumbnails could be recognised more quickly and accurately than thumbnails of the same parent images that had been reduced without treatment. This pretreatment in thumbnail creation is offered as an improvement. Burton, C.A. Johnston, L.J. Sonenberg, E.A. case study distortion experiment InfoVis data compression data visualisation edge detection edge-detecting smoothing image coding image databases image reduction image selection lossy image compression parent image treatment searching static codebook compression thumbnail creation thumbnail image recognition user interface user interfaces visual databases 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528694 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study. A WWW viewpoint on scientific visualization: an EPA case study for technology transfer. The paper examines how to provide scientific visualization capabilities to environmental scientists, policy analysts and decision makers with personal computers (PCs) on their desktops. An approach for using the World Wide Web (WWW) for disseminating knowledge on scientific visualization and for intelligent access to visualization capabilities on high performance (UNIX) workstations is outlined. Rhyne, T.M. case study world wide web InfoVis EPA case study Internet Unix World Wide Web data visualisation decision makers environmental science computing environmental scientists government data processing high performance UNIX workstations information dissemination information retrieval intelligent visualization capability access knowledge dissemination microcomputer applications operating systems (computers) personal computers policy analysts public administration scientific visualization capabilities technology transfer workstations 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528693 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study: fishing for information on the Internet. As the Internet continues to grow, the amount of accessible information becomes increasingly vast. Search tools exist that allow users to find relevant information. However, a search can often produce such a large amount of data that it becomes hard to ferret out the most appropriate and highest quality information. In addition, some search tools lose valuable information when displaying the results to the user. The paper describes a search visualization tool, called FISH, for viewing hierarchically structured information and managing information overload. FISH (Forager for the Information Super Highway) allows users to visualize the results of search requests across large document spaces in a way that preserves the structure of the information space. FISH displays the returned documents as rectangles, using a combination of order, indentation, size, and color to denote document hierarchy, the score of the documents with respect to the search, and other data attributes. In addition, the user can navigate through the document space for in-depth probing and refinement. Day, D. Hirschman, L. Mitchell, R. case study color document hierarchy InfoVis FISH search visualization tool Internet color data attributes data visualisation document hierarch hierarchically structured information viewing indentation information overload management information retrieval large document spaces online front-ends order returned documents search requests search tools size user navigation 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528692 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study. Visualising cyberspace: information visualisation in the Harmony Internet browser. The explosive growth of information systems on the Internet has clearly demonstrated the need to organise, filter, and present information in ways which allow users to cope with the sheer quantities of information available. The scope for visualisation of Gopher and WWW spaces is restricted by the limitations of their respective data models. The far richer data model supported by the Hyper-G Internet information system is exploited by its Harmony client to provide a number of tightly-coupled, two- and three-dimensional visualisation and navigational facilities, which help provide location feedback and alleviate user disorientation. Andrews, K. case study filter InfoVis Harmony Internet browser Harmony client Hyper-G Internet information system Internet WWW spaces cyberspace visualisation data models data structures data visualisation hypermedia information retrieval information systems information systemsGopher spaces information visualisation location feedback navigational facilities 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528691 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Case study. Narcissus: visualising information. It is becoming increasingly important that support is provided for users who are dealing with complex information spaces. The need is driven by the growing number of domains where there is a requirement for users to understand, navigate and manipulate large sets of computer based data; by the increasing size and complexity of this information and by the pressures to use this information efficiently. The paradigmatic example is the World Wide Web, but other domains include software systems, information systems and concurrent engineering. One approach to providing this support is to provide sophisticated visualisation tools which lead the users to form an intuitive understanding of the structure and behaviour of their domain and which provide mechanisms which allow them to manipulate objects within their system. The paper describes such a tool and a number of visualisation techniques that it implements. Beale, R. Drew, N.S. Hendley, R.J. Wood, A.M. case study world wide web InfoVis World Wide Web adaptive systems complex information spaces concurrent engineering data manipulation data visualisation information networks information retrieval information systems information visualisation intuitive understanding large computer based data sets navigation object manipulation programming environments self-adjusting systems software systems user support virtual reality visual programmingNarcissus visualisation tools 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528690 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Research report. DataSpace: 3-D visualizations of large databases. DataSpace is a system for interactive 3-D visualization and analysis of large databases. DataSpace utilizes the display space by placing panels of information, possibly generated by different visualization applications, in a 3-D graph layout, and providing continuous navigation facilities. Selective rearrangements and transparency can be used to reduce occlusion or to compare or merge a set of images (e.g. line graphs or scatter plots) that are aligned and stacked in depth. A prototype system supporting the basic 3-D graphic operations (layout, zoom, rotation, translation, transparency) has been implemented. We provide several illustrative examples of DataSpace displays taken from the current system. We present the 3-D display paradigm, describe the query, layout and rendering steps required to create a display, and discuss some performance issues. Anupam, V. Dar, S. Leibfried, T. Petajan, E. graph graph layout navigation occlusion zoom InfoVis 3D graph layout 3D graphic operations DataSpace computer displays continuous navigation facilities data analysis data visualisation display space image comparison image merging information panels interactive 3D visualization large database analysis large databases layout step performance issues query processing query step rendering step selective rearrangements transparency very large databases 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528689 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Research report. Interacting with huge hierarchies: beyond cone trees. The paper describes an implementation of a tool for visualizing and interacting with huge information hierarchies, and some preliminary empirical evaluation of the tool's efficacy. Existing systems for visualizing huge hierarchies using cone trees "break down" once the hierarchy to be displayed exceeds roughly 1000 nodes, due to increasing visual clutter. The paper describes a system called fsviz which visualizes arbitrarily large hierarchies while retaining user control. This is accomplished by augmenting cone trees with several graphical and interaction techniques: usage-based filtering, animated zooming, hand-coupled rotation, fish-eye zooming, coalescing of distant nodes, texturing, effective use of colour for depth cueing, and the applications of dynamic queries. The fsviz system also improves upon earlier cone tree visualization systems through a more elaborate node layout algorithm. This algorithm enhances the usefulness of cone tree visualization for large hierarchies by all but eliminating clutter. Carriere, J. Kazman, R. evaluation hierarchies hierarchy interaction zooming InfoVis animated zooming colour computer animation cone tree visualization systems cone trees data visualisation depth cueing distant node coalescence dynamic queries empirical evaluation fish-eye zooming fsviz graphical techniques hand-coupled rotation huge hierarchies huge information hierarchy interaction huge information hierarchy visualisation interaction techniques node layout algorithm texturing tool efficacy tree data structures usage-based filtering user control visual clutter 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528688 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) IVEE: an Information Visualization and Exploration Environment. The Information Visualization and Exploration Environment (NEE) is a system for automatic creation of dynamic queries applications. IVEE imports database relations and automatically creates environments holding visualizations and query devices. IVEE offers multiple visualizations such as maps and starfields, and multiple query devices, such as sliders, alphasliders, and toggles. Arbitrary graphical objects can be attached to database objects in visualizations. Multiple visualizations may be active simultaneously. Users can interactively lay out and change between types of query devices. Users may retrieve details-on-demand by clicking on visualization objects. An HTML file may be provided along with the database, specifying how details-on-demand information should be presented, allowing for presentation of multimedia information in database objects. Finally, multiple IVEE clients running on separate workstations on a network can communicate by letting one user's actions affect the visualization in an another IVEE client. Ahlberg, C. Wistrand, E. database network InfoVis HTML file IVEE Information Visualization and Exploration Environment alphasliders arbitrary graphical objects automatic dynamic query creation computer animation data visualisation database objects database relations details-on-demand retrieval maps multimedia computing multimedia information multiple IVEE clients multiple query devices multiple visualizations network query devices query formulation query processing sliders starfields toggles user actions workstations 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528687 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) VRMosaic: WEB access from within a virtual environment. Virtual reality can aid in designing large and complex structures such as ships, skyscrapers, factories, and aircraft. But before VR can realize this potential, we need to solve a number of problems. One of these problems: the user's need to see and interact with non-geometric information is examined. Our VR environment, RealEyes, can display large-scale and detailed geometry at reasonable frame rates (>20 Hz) allowing a user to see and navigate within a design from a first person perspective. However, much (if not most) of the information associated with a particular design has no geometric representation. This includes information such as schematics of electrical, hydraulic, and plumbing systems; information describing materials or processes; and descriptive (textual) information of other types. Many researchers have developed a wealth of techniques for presenting such data on flat-screen displays, but until recently, we have not had a means for naturally displaying such information within a VR environment. To make non-geometric data more available, we have implemented a version of Mosaic that functions within a fully immersive VR system. Our system, VRMosaic, allows a user of VR to access and display most of the data available using flat screen Mosaic. Moreover, we have made it extensible to allow for the seamless integration of specialized forms of data and interaction. This paper describes how we implemented VRMosaic using a VR-capable version of Interviews, It also describes some Mosaic-like uses of that system and some "non-Mosaic-like" extensions. Angus, I.G. Sowizral, H.A. interaction InfoVis RealEyes VR-capable Interviews VRMosaic Web access computer displays data visualisation detailed geometry display first person perspective flat screen Mosaic frame rates fully immersive VR system information networks large-scale geometry display nongeometric data nongeometric information virtual environment virtual reality 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528686 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Visualizing the non-visual: spatial analysis and interaction with information from text documents. The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language. Crow, V. Lantrip, D. Pennock, K. Pottier, M. Schur, A. Thomas, J. Wise, J.A. document interaction perception text InfoVis archived reports data visualisation digital libraries information interaction information retrieval informational characteristics large text document corpora libraries library automation natural world nonvisual visualization perception procedures regulations spatial analysis spatial representation text content text content spatialization text documents visual analysis visual browsing visual databases word processingaction 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528685 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) The information mural: a technique for displaying and navigating large information spaces. Visualizations which depict entire information spaces provide context for navigation and browsing tasks; however, the limited size of the display screen makes creating effective global views difficult. We have developed a technique for displaying and navigating large information spaces. The key concept is the use of an information mural, a two-dimensional reduced representation of an entire information space that fits completely within a display window or screen. Information murals use grayscale shading and color along with anti-aliasing techniques to create a miniature version of the entire data set. By incorporating navigational capabilities, information murals become a tool that can be used as a global view along with more detailed informational displays. Information murals are utilized in our software visualization research to help depict the execution of object-oriented programs, and can also be used in more general information visualization applications. Jerding, D.F. Stasko, J. color navigation software visualization InfoVis antialiasing antialiasing techniques browsing color graphics colour graphics data visualisation data visualization display screen global views grayscale shading information display information mural information navigation large information spaces object-oriented programming object-oriented programs software visualization user interfaces visual programming 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528684 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) SDM: malleable information graphics. Selective dynamic manipulation (SDM) is a paradigm for interacting with objects in visualizations. Its methods offer a high degree of selectivity, in choosing object sets, in the selection of interactive techniques and the properties they affect, and in the degree to which a user action affects the visualization. Our goal is to provide a flexible set of techniques and feedback mechanisms that enable users to move objects and transform their appearance to perform a variety of information analysis tasks. Chuah, M.C. Kolojejchick, J. Mattis, J. Roth, S.F. InfoVis SDM data visualisation direct manipulation graphical user interfaces information analysis tasks interactive systems interactive techniques malleable information graphics object set selection selective dynamic manipulation user action visualizations 1995 IEEE Symposium on Information Visualization (InfoVis) direct manipulation interactive techniques visualization 1995 infovis95--528683 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Research report: improving browsing in information by the automatic display layout. It is well known that graphical representations could be very helpful to browse in graph structured information. But this promising approach requires the capability of an automatic layout system because the tedious and time consuming task of a manual layout leads to a rejection of this approach by the user. In our approach, we split the task of retrieving information into two phases that are getting the orientation within the network and reading currently visited information. We present layout algorithms for both phases which have the benefit of being flexible and adaptable to individual user requests and ensure the topological consistency, i.e. the stability of the topology of the information layout during a sequence of display layouts. The results show that especially the possibility of an animation of the layout process can assist the user essentially in maintaining the orientation in the information network. Ernst, R. Luders, P. animation graph network InfoVis automatic display layout automatic layout system computer animation display layouts graph structured information graphical representations graphical user interfaces human factors information browsing information network information retrieval layout algorithms manual layout time consuming user model user modelling user requests 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528682 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Research report: information animation applications in the capital markets. 3D computer graphics can be extremely expressive. It is possible to display an entire securities market, like the S&P 500, on a single screen. With the correct approach to the visual design of the layout, these massive amounts of information can be quickly and easily comprehended by a human observer. By using motion and animated interaction, it is possible to use 3D as a reliable, accurate and precise decision-support tool. Information animation applications are particularly suited to the securities industry because that is where we find huge amounts of data, the value of which declines rapidly with time, and where critical decisions are being made on this data in very short periods of time. Information animation technology is an important new tool for the securities industry, where people need to be in the decision-making loop without suffering from information overload. Several examples are discussed including equity trading analytics, fixed income trading analytics and fixed-income risk viewing. Wright, W. animation interaction InfoVis 3D computer graphics capital markets computer animation data visualisation decision support systems decision-support tool equity trading analytics financial data processing fixed income trading analytics fixed-income risk viewing information animation applications risk management securities industry securities market securities trading three dimensional computer graphics visual design 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528681 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Towards a generative theory of diagram design. We describe the theoretical background for AVE, an automatic visualization engine for semantic networks. We have a functional notion of aesthetics and therefore understand meaningfulness as a central issue for information visualization. This implies that the diagrams should communicate the characteristics of the data as effectively as possible. In this generative theory of diagram design, we include data characterization, systematic use of graphical means of expression and the combination of graphical means of expression. After giving a brief introduction and an application scenario we discuss these aspects in detail. Finally, a process model of an automatic visualization process is sketched and directions for further research are outlined. Golovchinsky, G. Kamps, T. Reichenberger, K. aesthetics theory InfoVis CAD aesthetics automatic visualization engine automatic visualization process data characterization data visualisation diagram design diagrams information visualization process model semantic networks semantic networksAVE 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis95--528680 10/30/1995 1995 IEEE Symposium on Information Visualization (InfoVis) Visualisation for functional design. We present two novel visualisation tools: the Influence Explorer and the Prosection Matrix. These were specifically created to support engineering artifact design and similar tasks in which a set of parameter values must be chosen to lead to acceptable artifact performance. These tools combine two concepts. One is the interactive and virtually immediate responsive display of data in a manner conducive to the acquisition of insight. The other, involving the precalculation of samples of artifact performance, facilitates smooth exploration and optimisation leading to a design decision. The anticipated benefits of these visualisation tools are illustrated by an example taken from electronic circuit design, in which full account must be taken of the uncertainties in parameter values arising from inevitable variations in the manufacturing process. Dawkes, H. Spence, B. Su, H. Tweedie, L. insight matrix InfoVis CAD Influence Explorer Prosection Matrix circuit CAD data visualisation electronic circuit design engineering artifact design engineering graphics functional design interactive display interactive systems manufacturing process optimisation user interfaces visualisation tools 1995 IEEE Symposium on Information Visualization (InfoVis) 1995 infovis96--559229 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Visualizing a tennis match. This paper describes our work on visualizing the information of a tennis match. We use competition trees to organize the information of a tennis match and visualize the competition trees by the top-nesting layered maps with translucent colored layers. We create iconic representations to describe the detailed information of athletic events in an intuitive manner. Specialized views of the information are displayed by applying multiple Magic Lens filters on the top-nesting layered maps. The dynamic nature of the tennis match is depicted by the time-varying display. The approach we present in this paper can be used to visualize other sports information, information with competition property, or information with hierarchical structure. Banks, D.C. Jin, L. InfoVis athletic events competition property data visualisation iconic representations sport tennis match top-nesting layered maps tree data structures visualizing 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559228 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) DEPICT: Documents Evaluated as Pictures. Visualizing information using context vectors and self-organizing maps. HNC Software, Inc. has developed a system called DEPICT for visualizing the information content of large textual corpora. The system is built around two separate neural network methodologies: context vectors and self-organizing maps. Context vectors (CVs) are high dimensional information representations that encode the semantic content of the textual entities they represent. Self-organizing maps (SOMs) are capable of transforming an input, high dimensional signal space into a much lower (usually two or three) dimensional output space useful for visualization. Neither process requires human intervention, nor an external knowledge base. Together, these neural network techniques can be utilized to automatically identify the relevant information themes present in a corpus, and present those themes to the user in a intuitive visual form. Ilgen, M.R. Rushall, D.A. network InfoVis DEPICT context vectors data visualisation document handling documents evaluated as pictures information representations neural nets self organizing maps self-organising feature maps textual corpora textual entities visualization visualizing information 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559227 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Visualizing usability log data. Our approach to testing graphical user interfaces involves logging large amounts of data. These logs capture information at the key press and mouse click level about how an application is used. Since the raw data is voluminous and not at a useful level of detail, we use analysis and visualization to find information that is interesting and useful to a usability analyst but was previously buried in the data. We call some of our custom visualizations ?contextual? meaning they use key elements of the context the data was collected in as an organizing structure. We expect this type of visualization to be easier and faster to understand and more helpful than traditional charts. We hope that our finding a natural geometry for these visualizations will inspire others whose data apparently has no inherent geometry to find natural ways to visualize their data. Badre, A. Gray, M. Guzdial, M. usability InfoVis custom visualizations data analysis data logging data visualisation geometry graphical user interface testing graphical user interfaces human factors key press mouse click organizing structure program testingcharts usability analysis usability log data visualisation 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559226 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Visualizing the global topology of the MBone. We present a case study of visualizing the global topology of the Internet MBone. The MBone is the Internet's multicast backbone. Multicast is the most efficient way of distributing data from one sender to multiple receivers with minimal packet duplication. Developed and initially deployed by researchers within the Internet community, the MBone has been extremely popular for efficient transmission across the Internet of real-time video and audio streams such as conferences, meetings, congressional sessions, and NASA shuttle launches. The MBone, like the Internet itself grew exponentially with no central authority. The resulting suboptimal topology is of growing concern to network providers and the multicast research community. We create a geographic representation of the tunnel structure as arcs on a globe by resolving the latitude and longitude of MBone routers. The interactive 3D maps permit an immediate understanding of the global structure unavailable from the data in its original form as lines of text with only hostnames and IP addresses. Data visualization techniques such as grouping and thresholding allow further analysis of specific aspects of the MBone topology. We distribute the interactive 3D maps through the World-Wide Web using the VRML file format thus allowing network maintainers throughout the world to analyze the structure move effectively than would be possible with still pictures or pre-made videos. Claffy, K. Fenner, B. Hoffman, E. Munzner, T. case study geographic network text InfoVis IP addresses Internet Internet MBone MBone routers MBone topology visualisation NASA shuttle launches VRML World-Wide Web case study conferences congressional sessions data visualisation data visualization geographic representation hostnames interactive 3D maps interactive systems meetings minimal packet duplication multicast backbone multimedia communication multiple receivers real-time audio real-time systems real-time video suboptimal topology tunnel structure user interfaces virtual reality 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559225 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Geospatial metadata querying and visualization on the WWW using Java^TM applets. This paper presents the query and visualization interfaces of the Master Environmental Library (MEL) system. MEL uses the World Wide Web (WWW) to make accessible distributed data whose metadata conform to the Federal Geographic Data Committee's (FGDC) content standards for digital geospatial metadata. The interfaces are implemented as Java^TM applets and are more intuitive, interactive and possess greater functionality than their Hypertext Markup Language (HTML) counterparts. As well as querying, the interface allows users to visualize and manage the list of query results so that users can more quickly discover the datasets of real interest. Several new tools used to visualize attributes of the metadata are presented. Alper, N. Stein, C. geographic geospatial world wide web InfoVis Federal Geographic Data Committee HTML Hypertext Markup Language Internet Java applets Master Environmental Library system WWW World Wide Web content standards data visualisation datasets distributed data access distributed databases geographic information systems geospatial metadata querying geospatial metadata visualization hypermedia interactive system object-oriented languages page description languages query processing user interfaces visual databases 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559216 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Selection: 524,288 ways to say "this is interesting" . Visualization is a critical technology for understanding complex, data-rich systems. Effective visualizations make important features of the data immediately recognizable and enable the user to discover interesting and useful results by highlighting patterns. A key element of such systems is the ability to interact with displays of data by selecting a subset for further investigation. This operation is needed for use in linked-views systems and in drill-down analysis. It is a common manipulation in many other systems. It is as ubiquitous as selecting icons in a desktop GUI. It is therefore surprising to note that little research has been done on how selection can be implemented. This paper addresses this omission, presenting a taxonomy for selection mechanisms and discussing the interactions between branches of the taxonomy. Our suggestion of 524,288 possible systems [2^16 operation systems×2 (memory/memoryless)×2 (data-dependent/independent)×2 (brush/lasso)] is more in fun than serious, as within the taxonomy there are many different choices that can be made. This framework is the result of considering both the current state of the art and historical antecedents. Wills, G.J. taxonomy InfoVis complex data-rich systems data displays data manipulation data subset selection data visualisation data visualization desktop GUI drill-down analysis graphical user interfaces icon selection interesting results linked-views systems operation systems pattern highlighting recognizable data features selection mechanism taxonomy 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559215 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Distortion viewing techniques for 3-dimensional data. As the use of 3D information presentation becomes more prevalent, the need for effective viewing tools grows accordingly. Much work has been done in developing tools for 2D spaces which allow for detail in context views. We examine the extension of such 2D methods to 3D and explore the limitations encountered in accessing internal regions of the data with these methods. We then describe a novel solution to this problem of internal access with the introduction of a distortion function which creates a clear line of sight to the focus revealing sections previously obscured. The distortion is symmetric about the line of sight and is smoothly integrated back into the original 3D layout. Carpendale, S. Cowperthwaite, D.J. Fracchia, F.D. distortion InfoVis 3D information presentation 3D interaction computer displays context views data visualisation detail distortion viewing techniques focus graphical user interfaces information visualization internal access internal data regions line of sight screen layout smooth integration symmetric distortion function technical presentation three-dimensional displays user interface design user interface metaphors 1996 IEEE Symposium on Information Visualization (InfoVis) 3D interaction distortion viewing information visualization interface design issues interface metaphors screen layout 1996 infovis96--559214 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Techniques for non-linear magnification transformations. This paper presents efficient methods for implementing general non-linear magnification transformations. Techniques are provided for: combining linear and non-linear magnifications, constraining the domain of magnifications, combining multiple transformations, and smoothly interpolating between magnified and normal views. In addition, piecewise linear methods are introduced which allow greater efficiency and expressiveness than their continuous counterparts. Keahey, T.A. Robertson, E.L. InfoVis computer displays data visualisation domain constraint efficiency expressiveness interpolation magnifications combination multiple transformations combination nonlinear magnification transformations normal views piecewise linear methods piecewise-linear techniques smooth interpolation 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559213 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) On the semantics of interactive visualizations. Interactive techniques are powerful tools for manipulating visualizations to analyze, communicate and acquire information. This is especially true for large data sets or complex 3D visualizations. Although many new types of interaction have been introduced recently, very little work has been done on understanding what their components are, how they are related and how they can be combined. This paper begins to address these issues with a framework for classifying interactive visualizations. Our goal is a framework that will enable us to develop toolkits for assembling visualization interfaces both interactively and automatically. Chuah, M.C. Roth, S.F. interaction InfoVis automatic presentation systems complex 3D visualizations data visualisation graphics information acquisition information analysis information communication interactive systems interactive visualization large data sets semantics user interface management systems visualization interface construction toolkits 1996 IEEE Symposium on Information Visualization (InfoVis) automatic presentation systems graphic information visualization interactive techniques user interface 1996 infovis96--559212 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Rapid prototyping of information visualizations using VANISH. Discusses a software tool called VANISH (Visualizing And Navigating Information Structured Hierarchically), which supports the rapid prototyping of interactive 2D and 3D information visualizations. VANISH supports rapid prototyping through a special-purpose visual language called VaPL (VANISH Programming Language) tailored for visualizations, through a software architecture that insulates visualization-specific code from changes in both the domain being visualized and the presentation toolkit used, and through the reuse of visualization techniques between application domains. The generality of VANISH is established by showing how it is able to re-create a wide variety of ?standard? visualization techniques. VANISH's support for prototyping is shown through an extended example, where we build a C++ class browser, exploring many visualization alternatives in the process. Carriere, J. Kazman, R. toolkit InfoVis C++ class browser VANISH VaPL application domains data visualisation hierarchically structured information information navigation interactive information visualization interactive systems presentation toolkit rapid prototyping software architecture software prototyping software tool software tools visual language visual languages visual programming visualization techniques reuse visualization-specific code 1996 IEEE Symposium on Information Visualization (InfoVis) information visualization software tools visual programming languages 1996 infovis96--559211 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Data characterization for automatically visualizing heterogeneous information. Automated graphical generation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information in static or interactive environments. When building such a system, it is important to thoroughly understand the presentation-related characteristics of domain-specific information. We define a data-analysis taxonomy that can be used to characterize heterogeneous information. In addition to capturing the presentation-related properties of data, our characterization takes into account the user's information-seeking goals and visual-interpretation preferences. We use automatically-generated examples from two different application domains to demonstrate the coverage of the proposed taxonomy and its utility for selecting effective graphical techniques. Feiner, S.K. Zhou, M.X. taxonomy InfoVis application domains automated graphical generation systems automatic visualization automatically-generated examples data analysis data analysis taxonomy data characterization data visualisation domain-specific information graphical technique selection heterogeneous information human factors interactive environments presentation-related characteristics presentations design qualitative information quantitative information static environments technical presentation user information-seeking goals visual interpretation preferences 1996 IEEE Symposium on Information Visualization (InfoVis) 1996 infovis96--559210 10/28/1996 1996 IEEE Symposium on Information Visualization (InfoVis) Visage: a user interface environment for exploring information. Visage is a prototype user interface environment for exploring and analyzing information. It represents an approach to coordinating multiple visualizations, analysis and presentation tools in data-intensive domains. Visage is based on an information-centric approach to user interface design which strives to eliminate impediments to direct user access to information objects across applications and visualizations. Visage consists of a set of data manipulation operations, an intelligent system for generating a wide variety of data visualizations (SAGE) and a briefing tool that supports the conversion of visual displays used during exploration into interactive presentation slides. This paper presents the user interface components and styles of interaction that are central to Visage's information-centric approach. Burks, M.B. Dunmire, C. Gomberg, C.C. Kolojejchick, J. Lucas, P. Roth, S.F. Senn, J.A. Stroffolino, P.J. interaction InfoVis SAGE Visage briefing tool data analysis data conversion data manipulation operations data visualisation data-intensive domains direct user access information analysis information exploration information-centric approach intelligent system interaction styles interactive presentation slides interactive systems multiple data visualizations presentation tools technical presentation user interface design user interface environment user interface management systems visual display conversion 1996 IEEE Symposium on Information Visualization (InfoVis) exploratory data analysis graphics human-computer interaction user interface environment visualization 1996 infovis97--636794 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Metrics for effective information visualization. Metrics for information visualization will help designers create and evaluate 3D information visualizations. Based on experience from 60+ 3D information visualizations, the metrics we propose are: number of data points and data density; number of dimensions and cognitive overhead; occlusion percentage; and reference context and percentage of identifiable points. Brath, R. metrics occlusion InfoVis 3D information visualization cognitive overhead data density data points data visualisation design dimensions identifiable points information visualization metrics occlusion reference context software metrics 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636793 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Multidimensional detective. The display of multivariate datasets in parallel coordinates, transforms the search for relations among the variables into a 2-D pattern recognition problem. This is the basis for the application to visual data mining. The knowledge discovery process together with some general guidelines are illustrated on a dataset from the production of a VLSI chip. The special strength of parallel coordinates is in modeling relations. As an example, a simplified economic model is constructed with data from various economic sectors of a real country. The visual model shows the interelationship and dependencies between the sectors, circumstances where there is competition for the same resource, and feasible economic policies. Interactively, the model can be used to do trade-off analyses, discover sensitivities, do approximate optimization, monitor (as in a process) and provide decision support. Inselberg, A. data mining parallel coordinates InfoVis 2D pattern recognition problem VLSI chip approximate optimization competition data structures data visualisation decision support economic model economic policies economic sectors economics knowledge acquisition knowledge discovery modeling relations monitoring multidimensional detective multivariate dataset display parallel coordinates pattern recognition trade-off analyses very large databases visual data mining 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636792 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) The structure of the information visualization design space. Research on information visualization has reached the point where a number of successful point designs have been proposed and a variety of techniques have been discovered. It is now appropriate to describe and analyze portions of the design space so as to understand the differences among designs and to suggest new possibilities. This paper proposes an organization of the information visualization literature and illustrates it with a series of examples. The result is a framework for designing new visualizations and augmenting existing designs. Card, S.K. Mackinlay, J.D. InfoVis data visualisation information visualization design space information visualization literature morphological analysis point designs research taxonomy user interface 1997 IEEE Symposium on Information Visualization (InfoVis) design space information visualization morphological analysis taxonomy 1997 infovis97--636791 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Volume rendering for relational data. A method for efficiently volume rendering dense scatterplots of relational data is described. Plotting difficulties that arise from large numbers of data points, categorical variables, interaction with non-axis dimensions, and unknown values, are addressed by this method. The domain of the plot is voxelized using binning and then volume rendering. Since a table is used as the underlying data structure, no storage is wasted on regions with no data. The opacity of each voxel is a function of the number of data points in a corresponding bin. A voxel's color is derived by averaging the value of one of the variables for all the data points that fall in a bin. Other variables in the data may be mapped to external query sliders. A dragger object permits a user to select regions inside the volume. Becker, B.G. categorical color interaction InfoVis categorical variables data points data structure data structures data visualisation dense scatterplots external query sliders information visualization multivariate data nonaxis dimensions plotting query processing relational data relational databases rendering (computer graphics) table unknown values user interface volume rendering voxel 1997 IEEE Symposium on Information Visualization (InfoVis) information visualization multivariate data relational data scatterplot volume rendering 1997 infovis97--636790 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Design and evaluation of incremental data structures and algorithms for dynamic query interfaces. A dynamic query interface (DQI) is a database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQIs are elegant and powerful interfaces to small databases. Unfortunately, when applied to large databases, previous DQI algorithms slow to a crawl. We present a new incremental approach to DQI algorithms and display updates that work well with large databases, both in theory and in practice. Beigel, R. Shneiderman, B. Tanin, E. database dynamic query evaluation theory InfoVis DQI algorithms continuous real-time feedback data structures data visualisation database access mechanism dynamic query interfaces graphical user interfaces incremental data structures information visualization large databases query formulation query languages query processing real-time systems small databases user interfaces very large databases visual languages 1997 IEEE Symposium on Information Visualization (InfoVis) algorithm data structure database direct manipulation and dynamic query information visualization user interface 1997 infovis97--636789 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Domesticating Bead: adapting an information visualization system to a financial institution. The Bead visualization system employs a fast algorithm for laying out high-dimensional data in a low-dimensional space, and a number of features added to 3D visualizations to improve imageability. We describe recent work on both aspects of the system, in particular a generalization of the data types laid out and the implementation of imageability features in a 2D visualization tool. The variety of data analyzed in a financial institution such as UBS, and the ubiquity of spreadsheets as a medium for analysis, led us to extend our layout tools to handle data in a generic spreadsheet format. We describe the metrics of similarity used for this data type, and give examples of layouts of sets of records of financial trades. Conservatism and scepticism with regard to 3D visualization, along with the lack of functionality of widely available 3D web browsers, led to the development of a 2D visualization tool with refinements of a number of our imageability features. Brodbeck, D. Chalmers, M. Cotture, P. Lunzer, A. financial high-dimensional data metrics InfoVis 2D visualization tool 3D visualizations 3D web browsers Bead visualization system UBS data analysis data types data visualisation financial data processing financial institution graphical user interfaces high-dimensional data layout information visualization system low-dimensional space similarity metrics spreadsheet programs spreadsheets 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636788 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Coordinating declarative queries with a direct manipulation data exploration environment. Interactive visualization techniques allow data exploration to be a continuous process, rather than a discrete sequence of queries and results as in traditional database systems. However limitations in expressive power of current visualization systems force users to go outside the system and form a new dataset in order to perform certain operations, such as those involving the relationship among multiple objects. Further, there is no support for integrating data from the new dataset into previous visualizations, so users must recreate them. Visage's information centric paradigm provides an architectural hook for linking data across multiple queries, removing this overhead. This paper describes the addition to Visage of a visual query language, called VQE, which allows users to express more complicated queries than in previous interactive visualization systems. Visualizations can be created from queries and vice versa. When either is updated, the other changes to maintain consistency. Derthick, M. Kolojejchick, J. Roth, S.F. database InfoVis Visage consistency data analysis data exploration environment data visualisation database management systems database systems dataset declarative queries direct manipulation graphical user interfaces information centric paradigm interactive systems interactive visualization multiple objects query languages query processing visual languagesVQE visual query language 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636787 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Managing multiple focal levels in Table Lens. The Table Lens, focus+context visualization for large data tables, allows users to see 100 times as many data values as a spreadsheet in the same screen space in a manner that enables an extremely immediate form of exploratory data analysis. In the original Table Lens design, data are shown in the context area using graphical representations in a single pixel row. Scaling up the Table Lens technique beyond approximately 500 cases (rows) by 40 variables (columns) requires not showing every value individually and thus raises challenges for preserving the exploratory and navigational ease and power of the original design. We describe two design enhancements for introducing regions of less than a pixel row for each data value and discuss the issues raised by each. Rao, R. Tenev, T. focus+context pixel InfoVis Table Lens context visualization data analysis data values data visualisation design enhancements exploratory data analysis focus visualization graphical representations large data tables multiple focal level management screen space single pixel row spreadsheet user interface user interfaces very large databases 1997 IEEE Symposium on Information Visualization (InfoVis) Table Lens fisheye focus+context information visualization 1997 infovis97--636786 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Nonlinear magnification fields. We introduce nonlinear magnification fields as an abstract representation of nonlinear magnification, providing methods for converting transformation routines to magnification fields and vice-versa. This new representation provides ease of manipulation and power of expression. By removing the restrictions of explicit foci and allowing precise specification of magnification values, we can achieve magnification effects which were not previously possible. Of particular interest are techniques we introduce for expressing complex and subtle magnification effects through magnification brushing, and allowing intrinsic properties of the data being visualized to create data-driven magnifications. Keahey, T.A. Robertson, E.L. brushing InfoVis abstract representation data visualisation data visualization data-driven magnifications explicit foci magnification brushing magnification value specification manipulation nonlinear magnification fields transformation routines user interfaces 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636785 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Cacti: a front end for program visualization. We describe a system that allows the user to rapidly construct program visualizations over a variety of data sources. Such a system is a necessary foundation for using visualization as an aid to software understanding. The system supports an arbitrary set of data sources so that information from both static and dynamic analysis can be combined to offer meaningful software visualizations. It provides the user with a visual universal-relation front end that supports the definition of queries over multiple data sources without knowledge of the structure or contents of the sources. It uses a flexible back end with a range of different visualizations, most geared to the efficient display of large amounts of data. The result is a high-quality, easy-to-define program visualization that can address specific problems and hence is useful for software understanding. The overall system is flexible and extensible in that both the underlying data model and the set of visualizations are defined in resource files. Reiss, S.P. InfoVis Cacti data model data sources data visualisation dynamic analysis high-quality multiple data sources program diagnostics program visualization front end queries resource files reverse engineering software tools software understanding static analysis visual programming visual universal-relation front end 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636784 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) On integrating visualization techniques for effective software exploration. This paper describes the SHriMP visualization technique for seamlessly exploring software structure and browsing source code, with a focus on effectively assisting hybrid program comprehension strategies. The technique integrates both pan+zoom and fisheye-view visualization approaches for exploring a nested graph view of software structure. The fisheye-view approach handles multiple focal points, which are necessary when examining several subsystems and their mutual interconnections. Source code is presented by embedding code fragments within the nodes of the nested graph. Finer connections among these fragments are represented by a network that is navigated using a hypertext link-following metaphor. SHriMP combines this hypertext metaphor with animated panning and zooming motions over the nested graph to provide continuous orientation and contextual cues for the user. The SHriMP tool is being evaluated in several user studies. Observations of users performing program understanding tasks with the tool are discussed. Fracchia, F.D. Muller, H.A. Storey, M. Wong, K. fisheye graph network zoom zooming InfoVis SHriMP Simple Hierarchical MultiPerspective visualization animated panning data visualisation fisheye-view visualization graph theory graphical user interfaces hypermedia hypertext link-following metaphor multiple focal points nested graph view program comprehension program understanding reverse engineering software exploration software structure software tools source code browsing visual programming visualization techniques zooming 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636782 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Managing software with new visual representations. Managing large projects is a very challenging task requiring the tracking and scheduling of many resources. Although new technologies have made it possible to automatically collect data on project resources, it is very difficult to access this data because of its size and lack of structure. We present three novel glyphs for simplifying this process and apply them to visualizing statistics from a multi-million line software project. These glyphs address four important needs in project management: viewing time dependent data; managing large data volumes; dealing with diverse data types; and correspondence of data to real-world concepts. Chuah, M.C. Eick, S.G. statistics InfoVis data visualisation data visualization diverse data types large data volume management large project management multi-million line software project project management project resource tracking scheduling software development management statistical analysis statistics time dependent data viewing visual programming visual representations 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636778 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Adaptive information visualization based on the user's multiple viewpoints-interactive 3D visualization of the WWW. We introduce the adaptive information visualization method for hypermedia and the WWW based on the user's multiple viewpoints. We propose two graphical interfaces, the CVI and the RF-Cone. The CVI is the interface for interactive viewpoint selection. We can select a viewpoint reflecting our interests by using the CVI. According to the given viewpoint, the RF-Cone adaptively organizes the 3D representation of the hypermedia so that we can understand the semantic and structural relationship of the hypermedia and easily retrieve the information. Combining these methods, we have developed the WWW visualization system which can provide highly efficient navigation. Maruyama, M. Teraoka, T. navigation InfoVis Internet RF-Cone World Wide Web adaptive information visualization method data visualisation graphical user interfaces human factors hypermedia information retrieval interactive 3D visualization interactive systemsCVI interactive viewpoint selection multiple viewpoints semantic relationship structural relationship 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636761 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) A spreadsheet approach to information visualization. In information visualization, as the volume and complexity of the data increases, researchers require more powerful visualization tools that enable them to more effectively explore multidimensional datasets. We discuss the general utility of a novel visualization spreadsheet framework. Just as a numerical spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visual forms of information. We show that the spreadsheet approach facilitates certain information visualization tasks that are more difficult using other approaches. Unlike traditional spreadsheets, which store only simple data elements and formulas in each cell, a visualization spreadsheet cell can hold an entire complex data set, selection criteria, viewing specifications, and other information needed for a full-fledged information visualization. Similarly, inter-cell operations are far more complex, stretching beyond simple arithmetic and string operations to encompass a range of domain-specific operators. We have built two prototype systems that illustrate some of these research issues. The underlying approach in our work allows domain experts to define new data types and data operations, and enables visualization experts to incorporate new visualizations, viewing parameters, and view operations. Barry, P. Chi, E.H. Konstan, J. Riedl, J. InfoVis complex data set data complexity data operations data structures data types data visualisation domain-specific operators information visualization inter-cell operations multidimensional datasets prototype systems selection criteria spreadsheet approach spreadsheet programs user interfaces view operations viewing parameters viewing specifications 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis97--636759 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) Visualizing information on a sphere. We describe a method for the visualization of information units on spherical domains which is employed in the banking industry for risk analysis, stock prediction and other tasks. The system is based on a quantification of the similarity of related objects that governs the parameters of a mass-spring system. Unlike existing approaches we initialize all information units onto the inner surface of two concentric spheres and attach them with springs to the outer sphere. Since the spring stiffnesses correspond to the computed similarity measures, the system converges into an energy minimum which reveals multidimensional relations and adjacencies in terms of spatial neighborhoods. Depending on the application scenario our approach supports different topological arrangements of related objects. In order to cope with large data sets we propose a blobby clustering mechanism that enables encapsulation of similar objects by implicit shapes. In addition, we implemented various interaction techniques allowing semantic analysis of the underlying data sets. Our prototype system IVORY is written in Java, and its versatility is illustrated by an example from financial service providers. Finger, J. Gross, M.H. Sprenger, T.C. clustering financial interaction InfoVis IVORY Java bank data processing banking industry blobby clustering mechanism data encapsulation data visualisation encapsulation energy minimum financial service providers information visualization large data sets mass-spring system multidimensional relations object-oriented languages object-oriented programming prototype system risk analysis risk management semantic analysis similarity measures spatial neighborhood sphere stock prediction topological arrangements 1997 IEEE Symposium on Information Visualization (InfoVis) blobby clustering hierarchies information visualization multidimensional information space physically-based systems 1997 infovis97--636718 10/20/1997 1997 IEEE Symposium on Information Visualization (InfoVis) H3: laying out large directed graphs in 3D hyperbolic space. We present the H3 layout technique for drawing large directed graphs as node-link diagrams in 3D hyperbolic space. We can lay out much larger structures than can be handled using traditional techniques for drawing general graphs because we assume a hierarchical nature of the data. We impose a hierarchy on the graph by using domain-specific knowledge to find an appropriate spanning tree. Links which are not part of the spanning tree do not influence the layout but can be selectively drawn by user request. The volume of hyperbolic 3-space increases exponentially, as opposed to the familiar geometric increase of euclidean 3-space. We exploit this exponential amount of room by computing the layout according to the hyperbolic metric. We optimize the cone tree layout algorithm for 3D hyperbolic space by placing children on a hemisphere around the cone mouth instead of on its perimeter. Hyperbolic navigation affords a Focus+Context view of the structure with minimal visual clutter. We have successfully laid out hierarchies of over 20,000 nodes. Our implementation accommodates navigation through graphs too large to be rendered interactively by allowing the user to explicitly prune or expand subtrees. Munzner, T. focus+context graph hierarchies hierarchy navigation InfoVis 3D hyperbolic space H3 layout technique cone tree layout algorithm data visualisation data visualization diagrams directed graphs domain-specific knowledge euclidean 3-space graph drawing hierarchical data hyperbolic navigation large directed graphs node-link diagram optimisation optimization spanning tree subtree pruning trees (mathematics) visual clutter 1997 IEEE Symposium on Information Visualization (InfoVis) 1997 infovis98--729570 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Multi-faceted insight through interoperable visual information analysis paradigms. To gain insight and understanding of complex information collections, users must be able to visualize and explore many facets of the information. The paper presents several novel visual methods from an information analyst's perspective. The authors present a sample scenario, using the various methods to gain a variety of insights from a large information collection. They conclude that no single paradigm or visual method is sufficient for many analytical tasks. Often a suite of integrated methods offers a better analytic environment in today's emerging culture of information overload and rapidly changing issues. They also conclude that the interactions among these visual paradigms are equally as important as, if not more important than, the paradigms themselves. Hetzler, E. Martucci, L. Thomas, J. Whitney, P. insight InfoVis complex information collections data visualisation information analysis information exploration information overload information visualization integrated methods interoperable visual information analysis paradigms large information collection multi-faceted insight 1998 IEEE Symposium on Information Visualization (InfoVis) document analysis information analysis information visualization user scenario 1998 infovis98--729569 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) BiblioMapper: a cluster-based information visualization technique. The purpose of the paper is to develop a visualization system of a document space, called BiblioMapper, for CISI collections, one of the bibliographic databases available on the Internet. The major function of BiblioMapper is to visualize the document space with a cluster-based visualization technique. The cluster-based visualization technique assembles a set of documents according to semantic similarities. One advantage of this technique is that users are able to focus on and assess each cluster and the documents which the cluster comprises according to their information needs. Song, M. cluster document InfoVis BiblioMapper CISI collections Internet bibliographic databases bibliographic systems cluster-based information visualization technique data visualisation document image processing document space document space visualization information needs information retrieval semantic similarities 1998 IEEE Symposium on Information Visualization (InfoVis) clustering algorithms information retrieval textual information visualization 1998 infovis98--729568 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) The shape of Shakespeare: visualizing text using implicit surfaces . Information visualization focuses on the use of visual means for exploring non-visual information. While free-form text is a rich, common source of information, visualization of text is a challenging problem since text is inherently non-spatial. The paper explores the use of implicit surface models for visualizing text. The authors describe several techniques for text visualization that aid in understanding document content and document relationships. A simple method is defined for mapping document content to shape. By comparing the shapes of multiple documents, global content similarities and differences may be noted. In addition, they describe a visual clustering method in which documents are arranged in 3D based upon similarity scoring. Documents deemed closely related blend together as a single connected shape. Hence, a document corpus becomes a collection of shapes that reflect inter-document relationships. These techniques provide methods to visualize individual documents as well as corpus meta-data. They then combine the two techniques to produce transparent clusters enclosing individual document shapes. This provides a way to visualize both local and global contextual information. Finally, they elaborate on several potential applications of these methods. Ebert, D.S. Rohrer, R.M. Sibert, J.L. clustering document text InfoVis 3D arrangement connected shape corpus meta-data data visualisation document content document content mapping document corpus document relationships free-form text global content differences global content similarities global contextual information implicit surfaces information visualization inter-document relationships local contextual information nonvisual information shape similarity scoring text analysis text visualization transparent clusters visual clustering method 1998 IEEE Symposium on Information Visualization (InfoVis) blobby models document clustering graphics implicit surface modeling information retrieval information visualization procedural visualization text visualization user interface 1998 infovis98--729567 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) LensBar-visualization for browsing and filtering large lists of data. The author proposes a simple and powerful graphical interface tool called the LensBar for filtering and visualizing large lists of data. Browsing and querying are the most important tasks in retrieving information and LensBar integrates the two techniques into a simple scroll window with slider. While it looks familiar to users of conventional graphical interface tools, its filtering and zooming features offer sophisticated handling of large lists of textual data. Masui, T. zooming InfoVis LensBar browsing data visualisation filtering features graphical interface tool graphical user interfaces information retrieval large data list filtering large data list visualization list processing querying scroll window slide textual data zooming features 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729566 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Comparative visualization of protein structure-sequence alignments . Protein fold recognition (threading) involves the prediction of a protein's three-dimensional shape based on its similarity to a protein whose structure is known. Fold predictions are low resolution; no effort is made to rotate the protein's component amino acid side chains into their correct spatial orientations. Rather, the goal is to recognize the protein family member that most closely resembles the target sequence of unknown structure and to create a sensible alignment of the target to the structure (i.e., a structure-sequence alignment). To complement this structure prediction method the authors have implemented a low resolution molecular graphics tool. Since amino acid side chain orientation is not relevant in fold recognition, amino acid residues are represented by abstract shapes or glyphs much like Lego^TM blocks. They also borrow techniques from comparative streamline visualization to provide clean depictions of the entire protein structure model. By creating a low resolution representation of protein structure, they are able to approximately double the amount of information on the screen. This implementation also possesses the advantage of eliminating distracting and possibly misleading visual clutter resulting from the mapping of protein alignment information onto a high resolution display of a known structure. Hansen, M. Meads, D. Pang, A. InfoVis 3D shape prediction abstract shapes amino acid side chain rotation biology computing comparative streamline visualization comparative visualization data visualisation glyphs high resolution display low resolution molecular graphics tool low resolution representation molecular biophysics molecular configurations protein alignment information mapping protein fold recognition protein similarity protein structure-sequence alignments proteins spatial orientations target sequence threading 1998 IEEE Symposium on Information Visualization (InfoVis) alignment amino acids fold recognition glyphs proteins ribbons similarity streamlines structure threading 1998 infovis98--729565 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Visualizing decision table classifiers. Decision tables, like decision trees or neural nets, are classification models used for prediction. They are induced by machine learning algorithms. A decision table consists of a hierarchical table in which each entry in a higher level table gets broken down by the values of a pair of additional attributes to form another table. The structure is similar to dimensional stacking. A visualization method is presented that allows a model based on many attributes to be understood even by those unfamiliar with machine learning. Various forms of interaction are used to make this visualization more useful than other static designs. Becker, B.G. interaction machine learning InfoVis attributes classification models data visualisation decision table classifier visualization decision tables dimensional stacking hierarchical table interaction learning (artificial intelligence) machine learning algorithms pattern classification prediction 1998 IEEE Symposium on Information Visualization (InfoVis) classifier data mining decision table dimensional stacking relational data trellis displays 1998 infovis98--729564 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Saying it in graphics: from intentions to visualizations. The authors propose a methodology for automatically realizing communicative goals in graphics. It features a task model that mediates the communicative intent and the selection of graphical techniques. The methodology supports the following functions: isolating assertions presentable in graphics; mapping such assertions into tasks for the potential reader, and selecting graphical techniques that support those tasks. They illustrate the methodology by redesigning a textual argument into a multimedia one with the same rhetorical and content structures but employing graphics to achieve some of the intentions. Carenini, G. Green, N. Kerpedjiev, S. Moore, J. Roth, S.F. InfoVis assertions automatic communicative goal realization content structures data visualisation graphics intentions multimedia argument multimedia computing rhetorical structures task model textual argument redesign visualizations 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729563 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Geographic visualization: designing manipulable maps for exploring temporally varying georeferenced statistics. Geographic visualization, sometimes called cartographic visualization, is a form of information visualization in which principles from cartography, geographic information systems (GIS), exploratory data analysis (EDA), and information visualization more generally are integrated in the development and assessment of visual methods that facilitate the exploration, analysis, synthesis, and presentation of georeferenced information. The authors report on development and use of one component of a prototype GVis environment designed to facilitate exploration, by domain experts, of time series multivariate georeferenced health statistics. Emphasis is on how manipulable dynamic GVis tools may facilitate visual thinking, pattern noticing, and hypothesis generation. The prototype facilitates the highlighting of data extremes, examination of change in geographic patterns over time, and exploration of similarity among georeferenced variables. A qualitative exploratory analysis of verbal protocols and transaction logs is used to characterize system use. Evidence produced through the characterization highlights differences among experts in data analysis strategies (particularly in relation to the use of attribute ?focusing? combined with time series animation) and corresponding differences in success at noticing spatiotemporal patterns. Boscoe, F.P. Haug, D. MacEachren, A.M. Pickle, L. animation geographic statistics time series InfoVis cartographic visualization cartography computer animation data analysis data analysis strategies data extremes data visualisation experts exploratory data analysis geographic information systems geographic pattern changes geographic visualization georeferenced information georeferenced variable similarity health care hypothesis generation information visualization manipulable dynamic GVis tools manipulable map design medical information systems pattern noticing qualitative exploratory analysis spatiotemporal patterns statistical databases system use temporally varying georeferenced statistics exploration time series time series multivariate georeferenced health statistics transaction logs verbal protocols visual thinking 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729562 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) IVORY-an object-oriented framework for physics-based information visualization in Java. We present IVORY a newly developed, platform-independent framework for physics based visualization. IVORY is especially designed for information visualization applications and multidimensional graph layout. It is fully implemented in Java 1.1 and its architecture features client server setup, which allows us to run the visualization even on thin clients. In addition, VRML 2.0 exports can be viewed by any VRML plugged-in WWW browser. Individual visual metaphors are invoked into IVORY via an advanced plug-in mechanism, where plug-ins can be implemented by any experienced user. The configuration of IVORY is accomplished using a script language, called IVML. Some interactive visualization examples, such as the integration of a haptic interface illustrate the performance and versatility of our system. Our current implementation supports NT 4.0. Bielser, D. Gross, M.H. Sprenger, T.C. Strasser, T. graph graph layout InfoVis IVML IVORY Java NT 4 VRML 2 exports VRML plugged-in WWW browser advanced plug-in mechanism client server setup client-server systems data visualisation haptic interface information visualization applications interactive systems interactive visualization examples multidimensional graph layout object oriented framework physics based visualization physics computing platform-independent framework script language thin clients virtual reality languages visual metaphors 1998 IEEE Symposium on Information Visualization (InfoVis) multidimensional information modeling object-oriented visualization toolkit physics-based graph layout three-dimensional information visualization time varying data 1998 infovis98--729561 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Algorithm visualization for distributed environments. The paper investigates the visualization of distributed algorithms. We present a conceptual model and a system, VADE, that realizes this model. Since in asynchronous distributed systems there is no way of knowing (let alone, visualizing) the ?real? execution, we show how to generate a visualization which is consistent with the execution of the distributed algorithm. We also present the design and implementation of our system. VADE is designed so that the algorithm runs on the server's machines while the visualization is executed on a Web page on the client's machine. Programmers can write animations quickly and easily with the assistance of VADE's libraries. Moses, Y. Polunsky, Z. Tal, A. Ulitsky, L. InfoVis Internet Web page algorithm visualization animations asynchronous distributed systems client machine client-server systems computer animation conceptual model distributed algorithms distributed environments program visualisationVADE server machines 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729560 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) An operator interaction framework for visualization systems. Information visualization encounters a wide variety of different data domains. The visualization community has developed representation methods and interactive techniques. As a community, we have realized that the requirements in each domain are often dramatically different. In order to easily apply existing methods, researchers have developed a semiology of graphic representations. We have extended this research into a framework that includes operators and interactions in visualization systems, such as a visualization spreadsheet. We discuss properties of this framework and use it to characterize operations spanning a variety of different visualization techniques. The framework developed in the paper enables a new way of exploring and evaluating the design space of visualization operators, and helps end users in their analysis tasks. Chi, E.H. Riedl, J. interaction InfoVis analysis tasks data domains data visualisation design space end users graphic representations human factors information visualization interactive systems interactive techniques operator interaction framework representation methods spreadsheet programs user interfaces visualization community visualization operators visualization spreadsheet visualization systems visualization techniques 1998 IEEE Symposium on Information Visualization (InfoVis) design extensibility framework information visualization operators spreadsheet user interaction view/value visualization systems 1998 infovis98--729559 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Similarity clustering of dimensions for an enhanced visualization of multidimensional data. The order and arrangement of dimensions (variates) is crucial for the effectiveness of a large number of visualization techniques such as parallel coordinates, scatterplots, recursive pattern, and many others. We describe a systematic approach to arrange the dimensions according to their similarity. The basic idea is to rearrange the data dimensions such that dimensions showing a similar behavior are positioned next to each other. For the similarity clustering of dimensions, we need to define similarity measures which determine the partial or global similarity of dimensions. We then consider the problem of finding an optimal one- or two-dimensional arrangement of the dimensions based on their similarity. Theoretical considerations show that both, the one- and the two-dimensional arrangement problem are surprisingly hard problems, i.e. they are NP complete. Our solution of the problem is therefore based on heuristic algorithms. An empirical evaluation using a number of different visualization techniques shows the high impact of our similarity clustering of dimensions on the visualization results. Ankerst, M. Berchtold, S. Keim, D.A. clustering evaluation parallel coordinates InfoVis NP complete computational complexity data dimensions data mining data visualisation enhanced visualization global similarity hard problems heuristic algorithms multidimensional data parallel coordinates recursive pattern scatterplot similar behavior similarity clustering similarity measures systematic approach two dimensional arrangement visualization techniques 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729558 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) The generalized detail in-context problem. The paper describes a general formulation of the ?detail-in-context? problem, which is a central issue of fundamental importance to a wide variety of nonlinear magnification systems. A number of tools are described for dealing with this problem effectively. These tools can be applied to any continuous nonlinear magnification system, and are not tied to specific implementation features of the system that produced the original transformation. Of particular interest is the development of ?seamless multi level views?, which allow multiple global views of an information space (each having different information content) to be integrated into a single view without discontinuity. Keahey, T.A. InfoVis continuous nonlinear magnification system data visualisation general formulation generalized detail in-context problem information content information space interactive systems multiple global views nonlinear magnification systems seamless multi level views user interfaces 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729557 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Dynamic aggregation with circular visual designs. One very effective method for managing large data sets is aggregation or binning. We consider two aggregation methods that are tightly coupled with interactive manipulation and the visual representation of the data. Through this integration we hope to provide effective support for the aggregation process, specifically by enabling: 1) automatic aggregation, 2) continuous change and control of the aggregation level, 3) spatially based aggregates, 4) context maintenance across different aggregate levels, and 5) feedback on the level of aggregation. Chuah, M.C. InfoVis aggregate levels aggregation level control aggregation methods aggregation process automatic aggregation binning circular visual designs context maintenance continuous change data visualisation dynamic aggregation feedback interactive manipulation interactive systems large data set management spatial data structures spatially based aggregates user interfaces visual representation 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729556 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) An interactive view for hierarchical clustering. The paper describes a visualization of a general hierarchical clustering algorithm that allows the user to manipulate the number of classes produced by the clustering method without requiring a radical re-drawing of the clustering tree. The visual method used, a space filling recursive division of a rectangular area, keeps the items under consideration at the same screen position, even while the number of classes is under interactive control. As well as presenting a compact representation of the clustering with different cluster numbers, this method is particularly useful in a linked views environment where additional information can be added to a display to encode other information, without this added level of detail being perturbed when changes are made to the number of clusters. Wills, G.J. cluster clustering InfoVis cluster numbers clustering method clustering tree compact representation data analysis hierarchical clustering interactive control interactive systems interactive view linked views environment pattern clustering program visualisation radical re-drawing rectangular area space filling recursive division visual method visualization 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729555 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Reconfigurable disc trees for visualizing large hierarchical information space. We present a new visualization technique, called RDT (Reconfigurable Disc Tree) which can alleviate the disadvantages of cone trees significantly for large hierarchies while maintaining its context of using 3D depth. In RDT, each node is associated with a disc, around which its children are placed. Using discs instead of cones as the basic shape in RDT has several advantages: significant reduction of occluded region, sharp increase in number of displayed nodes, and easy projection onto plane without visual overlapping. We show that RDT can greatly enhance user perception by transforming its shapes dynamically in several ways: (1) disc tree which can significantly reduce the occluded region by the foreground objects; (2) compact disc tree which can increase the number of nodes displayed on the screen; and (3) plane disc tree which can be mapped onto the plane without visual overlapping. We describe an implementation of our visualization system called VISIT (Visual Information System for reconfigurable dIsc tree). It provides 2D and 3D layouts for RDT and various user interface features such as tree reconfiguration, tree transformation, tree shading, viewing transformation, animation, selection and browsing which can enhance the user perception and navigation capabilities. We also evaluate our system using the following three metrics: percentage of occlusion, density of displayed nodes on a screen, and number of identifiable nodes. Jeong, C.-S. Pang, A. animation hierarchies metrics navigation occlusion perception InfoVis 2D layouts 3D depth 3D layouts RDT Reconfigurable Disc Tree VISIT Visual Information System for reconfigurable dIsc tree animation browsing children compact disc tree cone trees data visualisation displayed nodes foreground objects large hierarchical information space visualization large hierarchies occluded region plane disc tree reconfigurable disc trees tree data structures tree reconfiguration tree shading tree transformation trees (mathematics) user interface features user interfaces user navigation capabilities user perception viewing transformation visual overlapping visualization system visualization technique 1998 IEEE Symposium on Information Visualization (InfoVis) compact disc tree disc tree hierarchy information visualization plane disc tree 1998 infovis98--729554 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) Traversal-based visualization of data structures. Algorithm animation systems and graphical debuggers perform the task of translating program state into visual representations. While algorithm animations typically rely on user augmented source code to produce visualizations, debuggers make use of symbolic information in the target program. As a result, visualizations produced by debuggers often lack important semantic content, making them inferior to algorithm animation systems. The paper presents a method to provide higher level, more informative visualizations in a debugger using a technique called traversal based visualization. The debugger traverses a data structure using a set of user supplied patterns to identify parts of the data structure to be drawn a similar way. A declarative language is used to specify the patterns and the actions to take when the patterns are encountered. Alternatively, the user can construct traversal specifications through a graphical user interface to the declarative language. Furthermore, the debugger supports modification of data. Changes made to the on-screen representation are reflected in the underlying data. Appel, A.W. Korn, J. animation InfoVis algorithm animation systems algorithm animations computer animation data structure visualization data structures declarative language formal specification graphical debuggers graphical user interface graphical user interfaces high level languages informative visualizations on-screen representation program debugging program state program visualisation semantic content symbolic information traversal based visualization traversal specifications user augmented source code user supplied patterns visual representations 1998 IEEE Symposium on Information Visualization (InfoVis) 1998 infovis98--729553 10/19/1998 1998 IEEE Symposium on Information Visualization (InfoVis) WEBPATH-a three dimensional Web history. A number of usability studies report that many users of the WWW cannot find pages already visited, additionally many users cannot visualise where they are, or where they have been browsing. Currently, readily available WWW browsers provide history mechanisms that offer little or no support in the presentation and manipulation of visited sites. Manipulation and presentation of usage data, such as a browse history has been used in a number of cases to aid users in searching for previously attained data, and to teach or assist other users in their browse or searching techniques. The paper presents a virtual reality (VR) based application to be used alongside traditional Web browsers, which provides them with a flexibly tailorable real time visualisation of their history. Frecon, E. Smith, G. history usability InfoVis Internet WEBPATH WWW browsers WWW users browse history browsing data visualisation history mechanisms information retrieval online front-ends real-time systems searching techniques tailorable real time visualisation three dimensional Web history traditional Web browsers usability studies virtual reality virtual reality based application 1998 IEEE Symposium on Information Visualization (InfoVis) World Wide Web virtual environments visualization web browsing 1998 infovis99--801851 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Cluster and calendar based visualization of time series data. A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented. Van Selow, E.R. van Wijk, J.J. cluster insight time series InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801852 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Visualizing application behavior on superscalar processors. The advent of superscalar processors with out-of-order execution makes it increasingly difficult to determine how well an application is utilizing the processor and how to adapt the application to improve its performance. We describe a visualization system for the analysis of application behavior on superscalar processors. Our system provides an overview-plus-detail display of the application's execution. A timeline view of pipeline performance data shows the overall utilization of the pipeline. This information is displayed using multiple time scales, enabling the user to drill down from a high-level application overview to a focus region of hundreds of cycles. This region of interest is displayed in detail using an animated cycle-by-cycle view of the execution. This view shows how instructions are reordered and executed and how functional units are being utilized. Additional context views correlate instuctions in this detailed view with the relevant source code for the application. This allows the user to discover the root cause of the poor pipeline utilization and make changes to the application to improve its performance. This visualization system can be easily configured to display a variety of processor models and configurations. We demonstrate it for both the MXS and MMIX processor models. Bosch, R. Hanrahan, P. Rosenblum, M. Stolte, C. overview InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) computer systems visualization superscalar processors visualization systems 1999 infovis99--801853 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Sensemaking of evolving Web sites using visualization spreadsheets. In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. We extend and apply a cognitive model called ˇ°visual sensemakingˇ± to the visualization spreadsheet. We use the task of making sense of a large Web site as a concrete example throughout the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the visualization spreadsheet help users draw conclusions from the overall relationships of the entire information set. Card, S.K. Chi, E.H. sensemaking InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) World Wide Web information ecologies information visualization log file analysis sensemaking spreadsheet 1999 infovis99--801854 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Does animation help users build mental maps of spatial information? We examine how animating a viewpoint change in a spatial information system affects a user's ability to build a mental map of the information in the space. We found that animation improves users' ability to reconstruct the information space, with no penalty on task performance time. We believe that this study provides strong evidence for adding animated transitions in many applications with fixed spatial data where the user navigates around the data space. Bederson, B.B. Boltman, A. animation InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801855 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Evaluating a visualisation of image similarity as a tool for image browsing. A similarity metric based on the low-level content of images can be used to create a visualisation in which visually similar images are displayed close to each other. We are carrying out a series of experiments to evaluate the usefulness of this type of visualisation as an image browsing aid. The initial experiment, described, considered whether people would find a given photograph more quickly in a visualisation than in a randomly arranged grid of images. The results show that the subjects were faster with the visualisation, although in post-experiment interviews many of them said that they preferred the clarity and regularity of the grid. We describe an algorithm with which the best aspects of the two layout types can be combined. Basalaj, W. Rodden, K. Sinclair, D. Wood, K. experiment InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801856 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Domain analysis: a technique to design a user-centered visualization framework. Domain Analysis for Data Visualization (DADV) is a technique to use when investigating a domain where data visualizations are going to be designed and added to existing software systems. DADV was used to design the data visualization in VisEIO-LCA, which is a framework to visualize environmental data about products. Most of the visualizations are designed using the following stages: formatting data in tables, selecting visual structures, and rendering the data on the screen. Although many visualization authors perform implicit domain analysis, in this paper domain analysis is added explicitly to the process of designing visualizations with the goal of producing move usable software tools. Environmental Life-Cycle Assessment (LCA) is used as a test bed for this technique. Espinosa, O.J. Garrett, J.H., Jr. Hendrickson, C. InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801857 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) A framework for focus+context visualization. This paper aims to give a systematic account of focus+context visualization techniques, i.e. visualizations which aim to give users integrated visual access to details and context in a data set. We introduce the notion that there are different orders of information visualization with focus+context being a second-order visualization and provide a formal framework for describing and constructing focus+context visualizations. Bjork, S. Holmquist, L.E. Redstrom, J. focus+context InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801858 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Navigating hierarchies with structure-based brushes. Interactive selection is a critical component in exploratory visualization, allowing users to isolate subsets of the displayed information for highlighting, deleting, analysis, or focussed investigation. Brushing, a popular method for implementing the selection process, has traditionally been performed in either screen space or data space. We introduce the concept of a structure-based brush, which can be used to perform selection in hierarchically structured data sets. Our structure-based brush allows users to navigate hierarchies by specifying focal extents and level-of-detail on a visual representation of the structure. Proximity-based coloring, which maps similar colors to data that are closely related within the structure, helps convey both structural relationships and anomalies. We describe the design and implementation of our structure-based brushing tool. We also validate its usefulness using two distinct hierarchical visualization techniques, namely hierarchical parallel coordinates and tree-maps. Fua, Y.-H. Rundensteiner, E.A. Ward, M.O. brushing hierarchies parallel coordinates InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) brushing exploratory data analysis hierarchical representation interactive selection 1999 infovis99--801859 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Dynamic hierarchy specification and visualization. This paper describes concepts that underlie the design and implementation of an information exploration system that allows users to impose arbitrary hierarchical organizations on their data. Such hierarchies allow a user to embed important semantic information into the hierarchy definition. Our goal is to recognize the significance of this implicit information and to utilize it in the hierarchy visualization. The innovative features of our system include the dynamic modification of the hierarchy definitions and the definition and implementation of a set of layout algorithms that utilize semantic information implicit in the tree construction. Bergeron, R.D. Wilson, R.M. hierarchies hierarchy InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801860 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Cushion treemaps: visualization of hierarchical information. A new method is presented for the visualization of hierarchical information, such as directory structures and organization structures. Cushion treemaps inherit the elegance of standard treemaps: compact, space-filling displays of hierarchical information, based on recursive subdivision of a rectangular image space. Intuitive shading is used to provide insight in the hierarchical structure. During the subdivision, ridges are added per rectangle, which are rendered with a simple shading model. The result is a surface that consists of recursive cushions. The method is efficient, effective, easy to use and implement, and has a wide applicability. van Wijk, J.J. van de Wetering, H. insight InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801861 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) 3D interactive visualization for inter-cell dependencies of spreadsheets. This paper proposes a new technique to visualize dependencies among cells in a spreadsheet. In this way, the system firstly visualizes a spreadsheet on a plane in three-dimensional space, and draws arcs between interrelated cells. By allowing a user to select an arbitrary cell and lift it up with direct manipulation, the system utilizes the third dimension to ameliorate visual occlusion of crossing arcs. As the user lifts a focused cell up, the interrelated cells are lifted up together; thus hidden dataflow networks can be visually intelligible interactively. Because spreadsheets are aimed at calculation itself rather than appearances of outputs, their mechanism is relatively invisible and not obvious for ordinary users. Our visualization helps such users to understand structures and mechanism of spreadsheets. Matsushita, Y. Okada, K. Shiozawa, H. occlusion InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 3D user interface Natto View information visualization inter-cell dependencies lifting-up operation spreadsheet 1999 infovis99--801862 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Efficient multi-object dynamic query histograms. Dynamic queries offer continuous feedback during range queries, and have been shown to be effective and satisfying. Recent work has extended them to datasets of 100,000 objects and, separately, to queries involving relations among multiple objects. The latter work enables filtering houses by properties of their owners, for instance. Our primary concern is providing feedback from histograms during dynamic query. The height of each histogram bar shows the count of selected objects whose attribute value falls into a given range. Unfortunately, previous efficient algorithms for single object queries overcount in the case of multiple objects if for instance, a house has multiple owners. This paper presents an efficient algorithm that with high probability closely approximates the true counts. Derthick, M. Harrison, J. Moore, A. Roth, S.F. dynamic query InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801863 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Aggregate Towers: scale sensitive visualization and decluttering of geospatial data. We have developed a technique, Aggregate Towers, that allows geospatial data to be visualized across a range of map scales. We use a combination of data aggregation algorithms and dynamically aggregating data markers (e.g., icons or symbols) to accommodate interactive zooming by a user while maintaining a representation that remains intuitive, consistent across multiple scales and uncluttered. This approach implicitly generates multiple levels of overview displays from a single set of underlying data. Rayson, J.K. geospatial overview zooming InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) aggregation cartography data visualization information visualization zoom 1999 infovis99--801864 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) VisageWeb: visualizing WWW data in Visage. VisageWeb is an information-centric user interface to the World Wide Web built within the Visage data visualization environment. This paper traces the development of the VisageWeb project, using it to motivate an exploration of how an information-centric architecture copes with new visualization challenges. We conclude with a presentation of the VisageWeb prototype itself. Higgins, M. Lucas, P. Sean, J. world wide web InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) World Wide Web information visualization user interface 1999 infovis99--801865 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) The automated multidimensional detective. Automation has arrived to parallel coordinates. A geometrically motivated classifier is presented and applied, with both training and testing stages, to 3 real datasets. Our results compared to those from 33 other classifiers have the least error. The algorithm is based on parallel coordinates and has very low computational complexity in the number of variables and the size of the dataset-contrasted with the very high or unknown (often unstated) complexity of other classifiers, the low complexity enables the rule derivation to be done in near real-time hence making the classification adaptive to changing conditions, provides comprehensible and explicit rules-contrasted to neural networks which are ˇ°black boxesˇ±, does dimensionality selection-where the minimal set of original variables (not transformed new variables as in Principal Component Analysis) required to state the rule is found, orders these variables so as to optimize the clarity of separation between the designated set and its complement-this solves the pesky ˇ°ordering problemˇ± in parallel coordinates. The algorithm is display independent, hence it can be applied to very large in size and number of variables datasets. Though it is instructive to present the results visually, the input size is no longer display-limited as for visual data mining. Avidan, T. Inselberg, A. data mining parallel coordinates InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801866 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Visualizing association rules for text mining. An association rule in data mining is an implication of the form XˇćY where X is a set of antecedent items and Y is the consequent item. For years researchers have developed many tools to visualize association rules. However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antecedents. Thus, it is extremely difficult to visualize and understand the association information of a large data set even when all the rules are available. This paper presents a novel visualization technique to tackle many of these problems. We apply the technology to a text mining study on large corpora. The results indicate that our design can easily handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping. Thomas, J. Whitney, P. Wong, P.C. data mining interaction occlusion text InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801867 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) A Java-based visual mining infrastructure and applications. Many real-world KDD (Knowledge Discovery & Data Mining) applications involve the navigation of large volumes of information on the web, such as, Internet resources, hot topics, and telecom phone switches. Quite often users feel lost, confused and overwhelmed with displays that contain too much information. This paper discusses a new content-driven visual mining infrastructure called VisMine, that uses several innovative techniques: (1) hidden visual structure and relationships for uncluttering displays; (2) simultaneous visual presentations for high-dimensional knowledge discovery; and (3) a new visual interface to plug in existing graphic toolkits for expanding its use in a wide variety of visual applications. We have applied this infrastructure to three data mining visualization applications-topic hierarchy for document navigation, web-based trouble shooting, and telecom switch mining. Baker, J. D'Eletto, R. Dayal, U. Hao, M.C. Hsu, M. data mining document hierarchy navigation InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis99--801868 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) The sunflower visual metaphor, a new paradigm for dimensional compression. This paper introduces the Sunflower visual metaphor for information visualization. The visual metaphor is presented as an alternative to current techniques of dimensional compression and the visualization tools that employ them. The paper discusses the motivation for the Sunflower paradigm, its implementation and critical factors for producing an effective visualization. A primary driver in this research effort has been to develop a visualization tool that facilitates browsing, knowledge discovery, and that supports learning through sense making and integration of new information. Rose, S. InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) information retrieval information visualization knowledge management text visualization visualization 1999 infovis99--801869 10/24/1999 1999 IEEE Symposium on Information Visualization (InfoVis) Constellation: a visualization tool for linguistic queries from MindNet. Constellation is a visualization system for the results of queries from the MindNet natural language semantic network. Constellation is targeted at helping MindNet's creators and users refine their algorithms, as opposed to understanding the structure of language. We designed a special-purpose graph layout algorithm which exploits higher-level structure in addition to the basic node and edge connectivity. Our layout prioritizes the creation of a semantic space to encode plausibility instead of traditional graph drawing metrics like minimizing edge crossings. We make careful use of several perceptual channels both to minimize the visual impact of edge crossings and to emphasize highlighted constellations of nodes and edges. Guimbretiere, F. Munzner, T. Robertson, G. graph graph drawing graph layout metrics network InfoVis 1999 IEEE Symposium on Information Visualization (InfoVis) 1999 infovis00--885101 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) New methods for the visualization of electric power system information. One area in need of new research in information visualization is the operation and analysis of large-scale electric power systems. In analyzing power systems, one is usually confronted with a large amount of multivariate data. With systems containing tens of thousands of electrical nodes (buses), a key challenge is to present this data in a form so the user can assess the state of the system in an intuitive and quick manner. This is particularly true when trying to analyze relationships between actual network power flows, the scheduled power flows, and the capacity of the transmission system. With electric industry restructuring and the move towards having a single entity, such as an independent system operator or pool, operate a much larger system, this need has become more acute. This paper presents several power system visualization techniques to help in this task. These techniques include animation of power system flow values, contouring of bus and transmission line flow values, data aggregation techniques and interactive 3D data visualization. Overbye, T.J. Weber, J.D. animation network InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885102 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Collaborative geographic visualization: enabling shared understanding of environmental processes. We describe a prototype same-time/different-place collaborative geovisualization environment. We outline an approach to understanding use and usability and present results of interviews with domain experts about the ways in which collaborative visualization might enable groups to work at a distance. One goal for our research is to design an effective and flexible system that can support group work on environmental science research mediated through dynamic geovisualization displays. We are addressing this goal using a four-step human-centered system design process, modeled on that proposed by (Gabbard et al., 1999) for development and evaluation of virtual environments. The steps they delineate are: user task analysis; expert guideline-based evaluation; formative user-centered evaluation; and summative comparative evaluation. Abdo, H. Brewer, I. Gundrum, J. MacEachren, A.M. Otto, G. evaluation geographic geovisualization usability InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885103 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Interactive problem solving via algorithm visualization. COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore design space effectively, evaluate new design solutions, and learn important relationships among design criteria, search speed and solution quality. We thus propose that visualization can serve as a tool for interactive intelligence, ie., human-machine collaboration for solving complex problems. Lalanne, D. Pu, P. collaboration InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885104 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Metaphor-aware 3D navigation. Anyone who has ever experienced three-dimensional (3D) interfaces will agree that navigating in a 3D world is not a trivial task. The user interface of traditional 3D browsers provides simple navigation tools that allow the user to modify the camera parameters such as orientation, position and focal. Using these tools, it is frequent that, after some movements, the user is lost in the virtual 3D space and usually tries to restart from the beginning. We present how the 3D navigation problem is addressed in the context of the CyberNet project (Abel et al., 2000). Our underlying principle is to help the user navigate by adapting the navigation tool to the virtual world. We feel that the navigation schemes provided by the 3D browsers are too generic for some specific 3D tools and we have developed adaptive navigation features that are dependent on the 3D metaphor used for visualizing the information and on the user's task. Abel, P. Gros, P. Loisel, D. Paris, J.P. Russo dos Santos, C. Trichaud, N. navigation InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885105 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Creativity, complexity, and precision: information visualization for (landscape) architecture. Drawing on ethnographic studies of (landscape) architects at work, this paper presents a human-centered approach to information visualization. A 3D collaborative electronic workspace allows people to configure, save and browse arrangements of heterogeneous work materials. Spatial arrangements and links are created and maintained as an integral part of ongoing work with `live' documents and objects. The result is an extension of the physical information space of the architects' studio that utilizes the potential of electronic data storage, visualization and network technologies to support work with information in context. Buscher, M. Christensen, M. Mogensen, P. Orbaek, P. Shapiro, D. network InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) architecture context electronic workspace information visualization spatio-temporal order work materials 2000 infovis00--885086 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Polaris: a system for query, analysis and visualization of multi-dimensional relational databases. In the last several years, large multi-dimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation. The authors present Polaris, an interface for exploring large multi-dimensional databases that extends the well-known Pivot Table interface. The novel features of Polaris include an interface for constructing visual specifications of table based graphical displays and the ability to generate a precise set of relational queries from the visual specifications. The visual specifications can be rapidly and incrementally developed, giving the analyst visual feedback as they construct complex queries and visualizations. Hanrahan, P. Stolte, C. InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885087 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Getting portals to behave. Data visualization environments help users understand and analyze their data by permitting interactive browsing of graphical representations of the data. To further facilitate understanding and analysis, many visualization environments have special features known as portals, which are sub-windows of a data canvas. Portals provide a way to display multiple graphical representations simultaneously, in a nested fashion. This makes portals an extremely powerful and flexible paradigm for data visualization. Unfortunately, with this flexibility comes complexity. There are over a hundred possible ways each portal can be configured to exhibit different behaviors. Many of these behaviors are confusing and certain behaviors can be inappropriate for a particular setting. It is desirable to eliminate confusing and inappropriate behaviors. The authors construct a taxonomy of portal behaviors and give recommendations to help designers of visualization systems decide which behaviors are intuitive and appropriate for a particular setting. They apply these recommendations to an example setting that is fully visually programmable and analyze the resulting reduced set of behaviors. Finally, the authors consider a real visualization environment and demonstrate some problems associated with behaviors that do not follow their recommendations. Olston, C. Woodruff, A. taxonomy InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) data visualization multiple views portals 2000 infovis00--885088 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) A scalable framework for information visualization. The paper describes major concepts of a scalable information visualization framework. We assume that the exploration of heterogeneous information spaces at arbitrary levels of detail requires a suitable preprocessing of information quantities, the combination of different graphical interfaces and the illustration of the frame of reference of given information sets. The innovative features of our system include: dynamic hierarchy computation and user controlled refinement of those hierarchies for preprocessing unstructured information spaces; a new Focus+Context technique for visualizing complex hierarchy graphs; a new paradigm for visualizing information structures within their frame of reference; and a new graphical interface that utilizes textual similarities to arrange objects of high dimensional information space in 3-dimensional visualization space. Kreuseler, M. Lopez, N. Schumann, H. focus+context hierarchies hierarchy InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885089 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Visualizing massive multi-digraphs. We describe MGV, an integrated visualization and exploration system for massive multi-digraph navigation. MGV's only assumption is that the vertex set of the underlying digraph corresponds to the set of leaves of a predetermined tree T. MGV builds an out-of-core graph hierarchy and provides mechanisms to plug in arbitrary visual representations for each graph hierarchy slice. Navigation from one level to another of the hierarchy corresponds to the implementation of a drill-down interface. In order to provide the user with navigation control and interactive response, MGV incorporates a number of visualization techniques like interactive pixel-oriented 2D and 3D maps, statistical displays, multi-linked views, and a zoomable label based interface. This makes the association of geographic information and graph data very natural. MGV follows the client-server paradigm and it is implemented in C and Java-3D. We highlight the main algorithmic and visualization techniques behind the tools and point out along the way several possible application scenarios. Our techniques are being applied to multi-graphs defined on vertex sets with sizes ranging from 100 million to 250 million vertices. Abello, J. Korn, J. geographic graph hierarchy navigation pixel InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) graphs hierarchies massive data sets out-of-core algorithms visualization 2000 infovis00--885090 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Density functions for visual attributes and effective partitioning in graph visualization. Two tasks in graph visualization require partitioning: the assignment of visual attributes and divisive clustering. Often, we would like to assign a color or other visual attributes to a node or edge that indicates an associated value. In an application involving divisive clustering, we would like to partition the graph into subsets of graph elements based on metric values in such a way that all subsets are evenly populated. Assuming a uniform distribution of metric values during either partitioning or coloring can have undesired effects such as empty clusters or only one level of emphasis for the entire graph. Probability density functions derived from statistics about a metric can help systems succeed at these tasks. Herman, I. Marshall, M.S. Melancon, G. clustering color graph statistics InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) clustering graph navigation graph visualization metrics 2000 infovis00--885091 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. Radial, space-filling visualizations can be useful for depicting information hierarchies, but they suffer from one major problem. As the hierarchy grows in size, many items become small, peripheral slices that are difficult to distinguish. We have developed three visualization/interaction techniques that provide flexible browsing of the display. The techniques allow viewers to examine the small items in detail while providing context within the entire information hierarchy. Additionally, smooth transitions between views help users maintain orientation within the complete information space. Stasko, J. Zhang, E. focus+context hierarchies hierarchy interaction navigation radial InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885092 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) A taxonomy of visualization techniques using the data state reference model. In previous work, researchers have attempted to construct taxonomies of information visualization techniques by examining the data domains that are compatible with these techniques. This is useful because implementers can quickly identify various techniques that can be applied to their domain of interest. However, these taxonomies do not help the implementers understand how to apply and implement these techniques. The author extends and proposes a new way to taxonomize information visualization techniques by using the Data State Model (E.H. Chi and J.T. Reidl, 1998). In fact, as the taxonomic analysis in the paper will show, many of the techniques share similar operating steps that can easily be reused. The paper shows that the Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly. Chi, E.H. taxonomy InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) data state model information visualization operators reference model taxonomy techniques 2000 infovis00--885093 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) GADGET/IV: a taxonomic approach to semi-automatic design of information visualization applications using modular visualization environment. Since novice users of visualization systems lack knowledge and expertise in data visualization, it is a tough task for them to generate efficient and effective visualizations that allow them to comprehend information that is embedded in the data. Therefore, systems supporting the users to design appropriate visualizations are of great importance. The GADGET (Goal-oriented Application Design Guidance for modular visualization EnvironmenTs) system, which has been developed by the authors (1997), interactively helps users to design scientific visualization applications by presenting appropriate MVE (Modular Visualization Environment) prototypes according to the specification of the visualization goals expressed mainly with the Wehrend matrix (S. Wehrend & C. Lewis, 1990). This paper extends this approach in order to develop a system named GADGET/IV, which is intended to provide the users with an environment for semi-automatic design of information visualization (IV) applications. To this end, a novel goal-oriented taxonomy of IV techniques is presented. Also, an initial design of the system architecture and user assistance flow is described. The usefulness of the GADGET/IV system is illustrated with example problems of Web site access frequency analysis. Fujishiro, I. Furuhata, R. Ichikawa, Y. Takeshima, Y. matrix taxonomy InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885094 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Redefining the focus and context of focus+context visualization. The increasing diversity of computers, especially among small mobile devices such as mobile phones and PDAs, raise new questions about information visualization techniques developed for the desktop computer. Using a series of examples ranging from applications for ordinary desktop displays to web-browsers and other applications for PDAs, we describe how a focus+context technique, Flip Zooming, is changed due to the situation it is used in. Based on these examples, we discuss how the use of ˇ°focusˇ± and ˇ°contextˇ± in focus+context techniques change in order to fit new areas of use for information visualization. Bjork, S. Redstrom, J. focus+context zooming InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885095 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) From metaphor to method: cartographic perspectives on information visualization. By virtue of their spatio-cognitive abilities, humans are able to navigate through geographic space as well as meaningfully communicate geographic information represented in cartographic form. The current dominance of spatial metaphors in information visualization research is the result of the realization that those cognitive skills also have value in the exploration and analysis of non-geographic information. While mapping or landscape metaphors are routinely used in this field, there is a noticeable lack of consideration for existing cartographic expertise. This is especially apparent whenever problematic issues are encountered, such as graphic complexity or feature labeling. There are a number of areas in which a cartographic outlook could provide a valuable perspective. This paper discusses how geographic and cartographic notions may influence the design of visualizations for textual information spaces. Map projections, generalization, feature labeling and map design issues are discussed. Skupin, A. geographic InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885096 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Information content measures of visual displays. With an increase in the number of different visualization techniques, it becomes necessary to develop a measure for evaluating the effectiveness of visualizations. Metrics to evaluate visual displays were developed based on measures of information content developed by Shannon and used in communication theory. These measures of information content can be used to quantify the relative effectiveness of displays. Flowers, W.C. Yang-Pelaez, J. metrics theory InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885097 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Visualizing sequential patterns for text mining. A sequential pattern in data mining is a finite series of elements such as AˇćBˇćCˇćD where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment. Cowley, W. Foote, H. Jurrus, E. Thomas, J. Wong, P.C. clustering data mining text InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--885098 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) ThemeRiver: visualizing theme changes over time. ThemeRiverTM is a prototype system that visualizes thematic variations over time within a large collection of documents. The ˇ°riverˇ± flows from left to right through time, changing width to depict changes in thematic strength of temporally associated documents. Colored ˇ°currentsˇ± flowing within the river narrow or widen to indicate decreases or increases in the strength of an individual topic or a group of topics in the associated documents. The river is shown within the context of a timeline and a corresponding textual presentation of external events. Havre, S. Hetzler, E. Nowell, L. InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) timeline trend analysis visualization metaphors 2000 infovis00--885099 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Lighthouse: showing the way to relevant information. Lighthouse is an on-line interface for a Web-based information retrieval system. It accepts queries from a user, collects the retrieved documents from the search engine, organizes and presents them to the user. The system integrates two known presentations of the retrieved results, the ranked list and clustering visualization, in a novel and effective way. It accepts the user's input and adjusts the document visualization accordingly. We give a brief overview of the system. Allan, J. Leuski, A. clustering document overview InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis00--857699 10/09/2000 2000 IEEE Symposium on Information Visualization (InfoVis) Using Visualization to Detect Plagiarism in Computer Science Classes. This paper introduces a number of general methods for visualizing commonality in sets of text files. Each visualization simultaneously compares one file in the set to all other files in the set. These visualizations, which can be computed in O(n) time and space, are explained and then applied to the problem of detecting plagiarism in large computer science classes. A case study is presented and sample visualizations are provided. Finally, a new interactive tool that can be used to produce and manipulate these visualizations is presented. Abrams, M. Ribler, R. case study text InfoVis 2000 IEEE Symposium on Information Visualization (InfoVis) 2000 infovis01--963273 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Visualizing time-series on spirals. In this paper, we present a new approach for the visualization of time-series data based on spirals. Different to classical bar charts and line graphs, the spiral is suited to visualize large data sets and supports much better the identification of periodic structures in the data. Moreover, it supports both the visualization of nominal and quantitative data based on a similar visualization metaphor. The extension of the spiral visualization to 3D gives access to concepts for zooming and focusing and linking in the data set. As such, spirals complement other visualization techniques for time series and specifically enhance the identication of periodic patterns. Alexa, M. Muller, W. Weber, M. nominal time series zooming InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) data mining graph drawing information visualization visualization of time-series data 2001 infovis01--963274 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Change blindness in information visualization: a case study. Change blindness occurs when people do not notice changes in visible elements of a scene. If people use an information visualization system to compare document collection subsets partitioned by their time-stamps, change blindness makes it impossible for them to recognize even very major changes, let alone minor ones. We describe theories from cognitive science that account for the change blindness phenomenon, as well as solutions developed for two visual analysis tools, a dot plot ( SPIRE Galaxies) and landscape (ThemeView˘â) visualizations. Hetzler, E. Nowell, L. Tanasse, T. case study document InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963275 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Cluster stability and the use of noise in interpretation of clustering. A clustering and ordination algorithm suitable for mining extremely large databases, including those produced by microarray expression studies, is described and analyzed for stability. Data from a yeast cell cycle experiment with 6000 genes and 18 experimental measurements per gene are used to test this algorithm under practical conditions. The process of assigning database objects to an X,Y coordinate, ordination, is shown to be stable with respect to random starting conditions, and with respect to minor perturbations in the starting similarity estimates. Careful analysis of the way clusters typically co-locate, versus the occasional large displacements under different starting conditions are shown to be useful in interpreting the data. This extra stability information is lost when only a single cluster is reported, which is currently the accepted practice. However, it is believed that the approaches presented here should become a standard part of best practices in analyzing computer clustering of large data collections. Boyack, K.W. Davidson, G.S. Wylie, B.N. cluster clustering database experiment InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963277 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Visually encoding program test information to find faults in software. Large test suites are frequently used to evaluate software systems and to locate errors. Unfortunately, this process can generate a huge amount of data that is difficult to interpret manually. We have created a system, TARANTULA, that visually encodes test data to help find program errors. The system uses a principled color mapping to represent how source lines act in passed and failed tests. It also provides a flexible user interface for examining different perspectives that show the behavior of the source code on test sets, ranging from individual tests, to important subsets such as the set of failed tests, to the entire test suite. Eagan, J. Harrold, M.J. Jones, J.A. Stasko, J. color InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963278 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Getting along: composition of visualization paradigms. This paper describes how focus+context techniques can be composed with other high-level visualization paradigms to mutual advantage. Examples are given showing composition both with a pan&zoom system, and with a treemap implementation. The examples illustrate how focus+context can be used as an exploration and navigation tool within those paradigms. Keahey, T.A. focus+context navigation treemap zoom InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963279 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Animated exploration of dynamic graphs with radial layout. We describe a new animation technique for supporting interactive exploration of a graph. We use the wellknown radial tree layout method, in which the view is determined by the selection of a focus node. Our main contribution is a method for animating the transition to a new layout when a new focus node is selected. In order to keep the transition easy to follow, the animation linearly interpolates the polar coordinates of the nodes, while enforcing ordering and orientation constraints. We apply this technique to visualizations of social networks and of the Gnutella file-sharing network, and discuss the results from our informal usability tests. Dhamija, R. Fisher, D. Hearst, M. Yee, K.-P. animation graph network radial social usability InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) animation graph drawing interaction 2001 infovis01--963280 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Effective graph visualization via node grouping. We discuss four methodologies for the application of node grouping in graph visualization. In addition, we introduce techniques for force-directed and orthogonal drawing which use node grouping information and have been shown in experiments to perform better than previous techniques. Not only do these techniques have significantly improved performance with respect to standard aesthetic measures, but they also attain qualitative improvement. Six, J.M. Tollis, I.G. graph InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) experimental studies force-directed drawing graph drawing graph visualization node grouping orthogonal drawing 2001 infovis01--963281 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Visualization of state transition graphs. A new method for the visualization of state transition graphs is presented. Visual information is reduced by clustering nodes, forming a tree structure of related clusters. This structure is visualized in three dimensions with concepts from cone trees and emphasis on symmetry. The resulting visualization makes it easier to relate features in the visualization of the state transition graph to semantic concepts in the corresponding process and vice versa. van Ham, F. van Wijk, J.J. van de Wetering, H. clustering graph InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963282 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Graph sketches. We introduce the notion of Graph Sketches. They can be thought of as visual indices that guide the navigation of a multi-graph too large to fit on the available display. We adhere to the Visual Information-Seeking Mantra: Overview first, zoom and filter, then details on demand. Graph Sketches are incorporated into MGV, an integrated visualization and exploration system for massive multi-digraph navigation. We highlight the main algorithmic and visualization tasks behind the computation of Graph Sketches and illustrate several application scenarios. Graph Sketches will be used to guide the navigation of multi-digraphs defined on vertex sets with sizes ranging from 100 to 250 million vertices. Abello, J. Finocchi, I. Korn, J. filter graph navigation overview zoom InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) graphs hierarchies massive data sets visualization 2001 infovis01--963283 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Ordered treemap layouts. Treemaps, a space-filling method of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, and fail to preserve an ordering of the underlying data. This paper introduces the ordered treemap, which addresses these two shortcomings. The ordered treemap algorithm ensures that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials, we show that compared to other layout algorithms ordered treemaps are more stable while maintaining relatively favorable aspect ratios of the constituent rectangles. A second test set uses stock market data. Shneiderman, B. Wattenberg, M. treemap InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) hierarchies information visualization ordered treemaps treemap trees 2001 infovis01--963284 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Collapsible cylindrical trees: a fast hierarchical navigation technique. This paper proposes a new visualization and interaction technique for medium-sized trees, called Collapsible Cylindrical Trees (CCT). Child nodes are mapped on rotating cylinders, which will be dynamically displayed or hidden to achieve a useful balance of detail and context. Besides a comprehensible threedimensional visualization of trees, the main feature of CCT is a very fast and intuitive interaction with the displayed nodes. Only a single click is needed to reach every node and perform an action on it, such as displaying a web page. The CCT browsing technique was developed for interaction with web hierarchies but is not limited to this domain. We also present sample implementations of CCT using VRML, which show the usefulness of this intuitive tree navigation technique. Dachselt, R. Ebert, J. hierarchies interaction navigation InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 3D graphics VRML XML hierarchy interactive tree sitemap visualization web navigation 2001 infovis01--963285 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Botanical visualization of huge hierarchies. A new method for the visualization of huge hierarchical data structures is presented. The method is based on the observation that we can easily see the branches, leaves, and their arrangement in a botanical tree, despite of the large number of elements. The strand model of Holton is used to convert an abstract tree into a geometric model. Nonleaf nodes are mapped to branches and child nodes to subbranches. A naive application of this model leads to unsatisfactory results, hence it is tailored to suit our purposes better. Continuing branches are emphasized, long branches are contracted, and sets of leaves are shown as fruit. The method is applied to the visualization of directory structures. The elements, directories and files, as well as their relations can easily be extracted, thereby showing that the use of methods from botanical modeling can be effective for information visualization. Kleiberg, E. van Wijk, J.J. van de Wetering, H. hierarchies InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) botanical tree directory tree huge hierarchy logical tree phyllotaxis strands tree visualization 2001 infovis01--963286 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Semantic depth of field. We present a new technique called Semantic Depth of Field (SDOF) as an alternative approach to focus-andcontext displays of information. We utilize a well-known method from photography and cinematography (depth-offield effect) for information visualization, which is to blur different parts of the depicted scene in dependence of their relevance. Independent of their spatial locations, objects of interest are depicted sharply in SDOF, whereas the context of the visualization is blurred. In this paper, we present a flexible model of SDOF which can be easily adopted to various types of applications. We discuss pros and cons of the new technique, give examples of application, and describe a fast prototype implementation of SDOF. Hauser, H. Kosara, R. Miksch, S. InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) depth of field focus+context information visualization 2001 infovis01--963287 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Interactive visualization of multiple query results. This paper introduces a graphical method for visually presenting and exploring the results of multiple queries simultaneously. This method allows a user to visually compare multiple query result sets, explore various combinations among the query result sets, and identify the ˇ°bestˇ± matches for combinations of multiple independent queries. This approach might also help users explore methods for progressively improving queries by visually comparing the improvement in result sets. Havre, S. Hetzler, E. Jurrus, E. Miller, N. Perrine, K. InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963288 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Pixel bar charts: a new technique for visualizing large multi-attribute data sets without aggregation. Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data and present only a very limited number of data values (as in the case of bar charts). In addition, these graphics may have a high degree of overlap which may occlude a significant portion of the data values (as in the case of the x-y plots). In this paper, we therefore propose a generalization of traditional bar charts and x-y-plots which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. We show that, for an effective pixel placement, we have to solve complex optimization problems, and present an algorithm which efficiently solves the problem. Our application using real-world e-commerce data shows the wide applicability and usefulness of our new idea. Dayal, U. Hao, M.C. Hsu, M. Keim, D.A. Ladisch, J. pixel InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963289 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) An empirical comparison of three commercial information visualization systems. An empirical comparison of three commercial information visualization systems on three different databases is presented. The systems use different paradigms for visualizing data. Tasks were selected to be "ecologically relevant", i.e. meaningful and interesting in the respective domains. Users of one system turned out to solve problems significantly faster than users of the other two, while users of another system would supply significantly more correct answers. Reasons for these results and general observations about the studied systems are discussed. Kobsa, A. InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963290 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) A comparison of 2-D visualizations of hierarchies. This paper describes two experiments that compare four two-dimensional visualizations of hierarchies: organization chart, icicle plot, treemap, and tree ring. The visualizations are evaluated in the context of decision tree analyses prevalent in data mining applications. The results suggest that either the tree ring or icicle plot is equivalent to the organization chart. Barlow, T. Neville, P. data mining hierarchies treemap InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963291 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) 2D vs 3D, implications on spatial memory. Since the introduction of graphical user interfaces (GUI) and two-dimensional (2D ) displays, the concept of space has entered the information technology (IT) domain. Interactions with computers were re-encoded in terms of fidelity to the interactions with real environment and consequently in terms of fitness to cognitive and spatial abilities. A further step in this direction was the creation of three-dimensional (3D) displays which have amplified the fidelity of digital representations. However, there are no systematic results evaluating the extent to which 3D displays better support cognitive spatial abilities. The aim of this research is to empirically investigate spatial memory performance across different instances of 2D and 3D displays. Two experiments were performed. The displays used in the experimental situation represented hierarchical information structures. The results of the test show that the 3D display does improve performances in the designed spatial memory task. Lind, M. Tavanti, M. InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963292 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Case study: visualization for decision tree analysis in data mining. Decision trees are one of the most popular methods of data mining. Decision trees partition large amounts of data into smaller segments by applying a series of rules. Creating and evaluating decision trees benefits greatly from visualization of the trees and diagnostic measures of their effectiveness. This paper describes an application, EMTree Results Viewer, that supports decision tree analysis through the visualization of model results and diagnosis. The functionality of the application and the visualization techniques are revealed through an example of churn analysis in the telecommunications industry. Barlow, T. Neville, P. case study data mining InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963293 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Case study: e-commerce clickstream visualization. We have developed an interactive, scalable visualization tool for analyzing the behavior of users of a web site. Our system not only shows site topology and traffic flow, but by segmenting site traffic data based on user attributes, including demographic data and purchase history, we can present a more complete picture of web site usage. This can lead to a more focussed analysis that allows direct comparison between user segments, and ultimately a deeper understanding of how users interact with a site. The tool is designed for real world use, and we present a usage study of the tool by analyzing the data of a failed ˇ°dot-comˇ±. Becker, B.G. Brainerd, J. case study history InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963294 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Case study: design and assessment of an enhanced geographic information system for exploration of multivariate health statistics. An implementation of an interactive parallel coordinate plot linked with the ArcView˘ç geographic information system (GIS) is presented. The integrated geographic visualization system was created for the exploratory analysis of mortality data from specific cancers as they relate, specifically spatially, to other mortality causes and to demographic and socioeconomic risk factors. The linked and interactive parallel coordinate plot was tested with and compared to a similarly interactive and linked scatterplot in usability assessments designed to assess each representationˇŻs relative effectiveness for exploration of these data sets. Evidence from these studies suggests that multivariate, spatial, and/or time series exploration is enhanced through the use of the parallel coordinate plot linked to maps. Edsall, R.M. MacEachren, A.M. Pickle, L. case study geographic scatterplot statistics time series usability InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis01--963295 10/22/2001 2001 IEEE Symposium on Information Visualization (InfoVis) Graphic data display for cardiovascular system. Our multi-disciplinary group has developed a visual representation for cardiovascular physiological variables. This enhances a clinicianˇŻs ability to detect and rapidly respond to critical events. The integrated and intuitive display communicates a patientˇŻs cardiovascular state so that it is easily and quickly understood without prior training. The display is designed to show patterns of functional relationships that aid in the detection, diagnosis, and treatment of a critical event. Agutter, J. Bermudez, J. Drews, F. Strayer, D. Syroid, N. Westenskow, D. InfoVis 2001 IEEE Symposium on Information Visualization (InfoVis) 2001 infovis02--1173141 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Multiscale visualization using data cubes. Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale pan-and-zoom systems are effective because they directly support this approach. However generating abstract overviews of large data sets is difficult, and most systems take advantage of only one type of abstraction: visual abstraction. Furthermore, these existing systems limit the analyst to a single zooming path on their data and thus a single set of abstract views. This paper presents: (1) a formalism for describing multiscale visualizations of data cubes with both data and visual abstraction, and (2) a method for independently zooming along one or more dimensions by traversing a zoom graph with nodes at different levels of detail. As an example of how to design multiscale visualizations using our system, we describe four design patterns using our formalism. These design patterns show the effectiveness of multiscale visualization of general relational databases. Hanrahan, P. Stolte, C. Tang, D. graph overview zoom zooming InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173142 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Visualization schemas for flexible information visualization. Relational databases provide significant flexibility to organize, store, and manipulate an infinite variety of complex data collections. This flexibility is enabled by the concept of relational data schemas, which allow data owners to easily design custom databases according to their unique needs. However, user interfaces and information visualizations for accessing and utilizing databases have not kept pace with this level of flexibility. This paper introduces the concept of visualization schemas, based on the Snap-Together Visualization model, which are analogous to relational data schemas. Visualization schemas enable users to rapidly construct customized multiple-view visualizations for databases in a similarly flexible fashion without programming. Since the design of appropriate visualizations for a given database depends on the data schema, visualization schemas are a natural analogy to the data schema concept. Conklin, N. North, C. Saini, V. database InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173143 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Building a visual database for example-based graphics generation. Example-based graphics generation systems automatically create new information visualizations by learning from existing graphic examples. As part of the effort on developing a general-purpose example-based generation system, we are building a visual database of graphic examples. In this paper, we address two main issues involved in constructing such a database: example selection and example modeling. As a result, our work offers three unique contributions: First, we build a visual database that contains a diverse collection of well-designed examples. Second, we develop a feature-based scheme to model all examples uniformly and accurately. Third, our visual database brings several important implications to the area of information visualization. Chen, M. Feng, F. Zhou, M.X. database InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173144 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Efficient cartogram generation: a comparison. Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. We deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms to solve the continuous cartogram problem. The first one uses an iterative relocation of the vertices based on scanlines. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure. Keim, D.A. North, S.C. Panse, C. Schneidewind, J. distortion pixel InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173145 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Visualizing data with bounded uncertainty. Visualization is a powerful way to facilitate data analysis, but it is crucial that visualization systems explicitly convey the presence, nature, and degree of uncertainty to users. Otherwise, there is a danger that data will be falsely interpreted, potentially leading to inaccurate conclusions. A common method for denoting uncertainty is to use error bars or similar techniques designed to convey the degree of statistical uncertainty. While uncertainty can often be modeled statistically, a second form of uncertainty, bounded uncertainty, can also arise that has very different properties than statistical uncertainty. Error bars should not be used for bounded uncertainty because they do not convey the correct properties, so a different technique should be used instead. We describe a technique for conveying bounded uncertainty in visualizations and show how it can be applied systematically to common displays of abstract charts and graphs. Interestingly, it is not always possible to show the exact degree of uncertainty, and in some cases it can only be displayed approximately. Mackinlay, J.D. Olston, C. uncertainty InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) bounded uncertainty uncertainty visualization 2002 infovis02--1173146 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Graphical encoding for information visualization: an empirical study. Research in several areas provides scientific guidance for use of graphical encoding to convey information in an information visualization display. By graphical encoding we mean the use of visual display elements such as icon color, shape, size, or position to convey information about objects represented by the icons. Literature offers inconclusive and often conflicting viewpoints, including the suggestion that the effectiveness of a graphical encoding depends on the type of data represented. Our empirical study suggests that the nature of the users' perceptual task is more indicative of the effectiveness of a graphical encoding than the type of data represented. Hix, D. Nowell, L. Schulman, R. color InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173147 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) The illusion of perceived metric 3D structure. A large body of results on the characteristics of human spatial vision suggests that space perception is distorted. Recent studies indicate that the geometry of visual space is best understood as Affine. If this is the case, it has far reaching implications on how 3D visualizations can be successfully employed. For instance, all attempts to build visualization systems where users are expected to discover relations based on Euclidean distances or shapes will be ineffective. Because visualization can, and sometimes do, employ all possible types of depth information and because the results from vision research usually concentrates on one or two such types, three experiments were performed under near optimal viewing conditions. The aim of the experiments was twofold: To test whether the earlier findings generalize to optimal viewing conditions and to get a sense of the size of the error under such conditions. The results show that the findings do generalize and that the errors are large. The implications of these results for successful visualizations are discussed. Bingham, G.P. Forser, C. Lind, M. perception InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173148 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation. We present a novel tree browser that builds on the conventional node link tree diagrams. It adds dynamic rescaling of branches of the tree to best fit the available screen space, optimized camera movement, and the use of preview icons summarizing the topology of the branches that cannot be expanded. In addition, it includes integrated search and filter functions. This paper reflects on the evolution of the design and highlights the principles that emerged from it. A controlled experiment showed benefits for navigation to already previously visited nodes and estimation of overall tree topology. Bederson, B.B. Grosjean, J. Plaisant, C. evaluation experiment filter navigation InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173149 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Process visualization with levels of detail. We demonstrate how we apply information visualization techniques to process monitoring. Virtual instruments are enhanced using history encoding instruments are capable of displaying the current value and the value from the near past. Multi-instruments are capable of displaying several data sources simultaneously. Levels of detail for virtual instruments are introduced where the screen area is inversely proportional to the information amount displayed. Furthermore the monitoring system is enhanced by using: 3D anchoring attachment of instruments to positions on a 3D model, collision avoidance a physically based spring model prevents instruments from overlapping, and focus+context rendering - giving the user a possibility to examine particular instruments in detail without loosing the context information. Gröller, M.E. Hauser, H. Matkovic, K. Sainitzer, R. focus+context history InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) focus+context visualization information visualization levels of detail process visualization 2002 infovis02--1173150 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Case study: visualizing sets of evolutionary trees. We describe a visualization tool which allows a biologist to explore a large set of hypothetical evolutionary trees. Interacting with such a dataset allows the biologist to identify distinct hypotheses about how different species or organisms evolved, which would not have been clear from traditional analyses. Our system integrates a point-set visualization of the distribution of hypothetical trees with detail views of an individual tree, or of a consensus tree summarizing a subset of trees. Efficient algorithms were required for the key tasks of computing distances between trees, finding consensus trees, and laying out the point-set visualization. Amenta, N. Klingner, J. case study InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173151 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) InterRing: an interactive tool for visually navigating and manipulating hierarchical structures. Radial, space-filling (RSF) techniques for hierarchy visualization have several advantages over traditional node-link diagrams, including the ability to efficiently use the display space while effectively conveying the hierarchy structure. Several RSF systems and tools have been developed to date, each with varying degrees of support for interactive operations such as selection and navigation. We describe what we believe to be a complete set of desirable operations on hierarchical structures. We then present InterRing, an RSF hierarchy visualization system that supports a significantly more extensive set of these operations than prior systems. In particular, InterRing supports multi-focus distortions, interactive hierarchy reconfiguration, and both semi-automated and manual selection. We show the power and utility of these and other operations, and describe our on-going efforts to evaluate their effectiveness and usability. Rundensteiner, E.A. Ward, M.O. Yang, J. hierarchy navigation radial usability InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) multifocus distortion radial space-filling hierarchy visualization structure-based brushing 2002 infovis02--1173152 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) A space-optimized tree visualization. We describe a new method for the visualization of tree structured relational data. It can be used especially for the display of very large hierarchies in a 2-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limit screen resolution. We use the concept of enclosure to partition the entire display space into a collection of local regions that are assigned to all nodes in tree T for the display of their sub-trees and themselves. To enable the exploration of large hierarchies, we use a modified semantic zooming technique to view the detail of a particular part of the hierarchy at a time based on user's interest. Layout animation is also provided to preserve the mental map while the user is exploring the hierarchy by changing zoomed views. Huang, M.L. Nguyen, Q.V. animation hierarchies hierarchy zooming InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173153 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Beamtrees: compact visualization of large hierarchies. Beamtrees are a new method for the visualization of large hierarchical data sets. Nodes are shown as stacked circular beams, such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information. van Ham, F. van Wijk, J.J. hierarchies treemap user study InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173154 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Visualizing biosequence data using texture mapping. Data-mining of information by the process of pattern discovery in protein sequences has been predominantly algorithm based. We discuss a visualization approach, which uses texture mapping and blending techniques to perform visual data-mining on text data obtained from discovering patterns in protein sequences. This visual approach, investigates the possibilities of representing text data in three dimensions and provides new possibilities of representing more dimensions of information in text data visualization and analysis. We also present a generic framework derived from this visualization approach to visualize text in biosequence data. Gao, G.R. Thiagarajan, P.R. text InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173155 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Arc diagrams: visualizing structure in strings. This paper introduces a new visualization method, the arc diagram, which is capable of representing complex patterns of repetition in string data. Arc diagrams improve over previous methods such as dotplots because they scale efficiently for strings that contain many instances of the same subsequence. This paper describes design and implementation issues related to arc diagrams and shows how they may be applied to visualize such diverse data as music, text, and compiled code. Wattenberg, M. text InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) arc diagram code music sequence string text visualization 2002 infovis02--1173156 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Interactive information visualization of a million items. Existing information visualization techniques are usually limited to the display of a few thousand items. This article describes new interactive techniques capable of handling a million items (effectively visible and manageable on screen). We evaluate the use of hardware-based techniques available with newer graphics cards, as well as new animation techniques and non-standard graphical features such as stereovision and overlap count. These techniques have been applied to two popular information visualizations: treemaps and scatter plot diagrams; but are generic enough to be applied to other 2D representations as well. Fekete, J.-D. Plaisant, C. animation hardware InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173157 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Angular brushing of extended parallel coordinates. In this paper we present angular brushing for parallel coordinates (PC) as a new approach to highlighting rational data-properties, i.e., features which - in a non-separable way - depend on two data dimensions. We also demonstrate smooth brushing as an intuitive tool for specifying nonbinary degree-of-interest functions (for focus+context visualization). We also briefly describe our implementation as well as its application to the visualization of CFD data. Doleisch, H. Hauser, H. Ledermann, F. brushing focus+context parallel coordinates InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) brushing focus+context visualization information visualization linear correlations parallel coordinates 2002 infovis02--1173158 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Multiple foci drill-down through tuple and attribute aggregation polyarchies in tabular data. Information analysis often involves decomposing data into sub-groups to allow for comparison and identification of relationships. Breakdown Visualization provides a mechanism to support this analysis through user guided drill-down of polyarchical metadata. This metadata describes multiple hierarchical structures for organizing tuple aggregations and table attributes. This structure is seen in financial data, organizational structures, sport statistics, and other domains. A spreadsheet format enables comparison of visualizations at any level of the hierarchy. Breakdown Visualization allows users to drill-down a single hierarchy then pivot into another hierarchy within the same view. It utilizes a fix and move technique that allows users to select multiple foci for drill-down. Conklin, N. North, C. Prabhakar, S. financial hierarchy statistics InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173159 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) ACE: a fast multiscale eigenvectors computation for drawing huge graphs. We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE exhibits an improvement of something like two orders of magnitude over the fastest algorithms we are aware of; it draws graphs of millions of nodes in less than a minute. ACE finds an optimal drawing by minimizing a quadratic energy function. The minimization problem is expressed as a generalized eigenvalue problem, which is rapidly solved using a novel algebraic multigrid technique. The same generalized eigenvalue problem seems to come up also in other fields, hence ACE appears to be applicable outside of graph drawing too. Carmel, L. Harel, D. Koren, Y. graph graph drawing InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) Fiedler vector algebraic multigrid force-directed layout generalized eigenvalue problem graph drawing multiscale/multilevel optimization the Hall energy 2002 infovis02--1173160 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Visual unrolling of network evolution and the analysis of dynamic discourse. A new method for visualizing the class of incrementally evolving networks is presented. In addition to the intermediate states of the network it conveys the nature of the change between them by unrolling the dynamics of the network. Each modification is shown in a separate layer of a three-dimensional representation, where the stack of layers corresponds to a time line of the evolution. We focus on discourse networks as the driving application, but our method extends to any type of network evolving in similar ways. Brandes, U. Corman, S.R. network InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173161 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) A hybrid layout algorithm for sub-quadratic multidimensional scaling. Many clustering and layout techniques have been used for structuring and visualising complex data. This paper is inspired by a number of such contemporary techniques and presents a novel hybrid approach based upon stochastic sampling, interpolation and spring models. We use Chalmers' 1996 O(N2) spring model as a benchmark when evaluating our technique, comparing layout quality and run times using data sets of synthetic and real data. Our algorithm runs in O(NˇîN) and executes significantly faster than Chalmers' 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data. Chalmers, M. Morrison, A. Ross, G. clustering InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173162 10/28/2002 2001 IEEE Symposium on Information Visualization (InfoVis) Demystifying venture capital investing. Since the crash of the dot.coms, investors have gotten a lot more careful with where they place their money. Now more than ever it becomes really important for venture capitalists (VCs) to monitor the state of the startups market and continually update their investment strategy to suit the rapidly changing market conditions. This paper presents three new visualization metaphors (Spiral Map, TimeTicker, and Double Histogram) for monitoring the startups market. While we are focusing on the VC domain, the visual metaphors developed are general and can be easily applied to other domains. Chuah, M.C. InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis02--1173163 10/28/2002 2002 IEEE Symposium on Information Visualization (InfoVis) Visual path analysis. We describe a system for analyzing the flow of traffic through Web sites. We decomposed the general path analysis problem into a set of distinct subproblems, and created a visual metaphor for analyzing each of them. Our system works off of multiple representations of the clickstream, and exposes the path extraction algorithms and data to the visual metaphors as Web services. We have combined the visual metaphors into a Web-based "path analysis portal" that lets the user easily switch between the different modes of analysis. Eick, S.G. Keahey, T.A. InfoVis 2002 IEEE Symposium on Information Visualization (InfoVis) 2002 infovis03--1295823 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Developing architectural lighting representations. This paper reports on the development of a visualization system for architectural lighting designers. It starts by motivating the problem as both complex in its physics and social organization. Three iterations of prototypes for displaying time and space varying phenomena are discussed. Fieldwork is presented to identify where in practice they will be most effective. A set of user studies, one of which is analyzed in fine-grained detail, show how building designers incorporate visualization on hypothetical design problems. This has positive implications for both energy efficiency and lighting quality in buildings. Canny, J. Do, E.Y. Glaser, D.C. Tan, R. social InfoVis information visualization 2003 IEEE Symposium on Information Visualization (InfoVis) architectural lighting design energy efficiency ethnographics fieldwork information visualization qualitative analysis 2003 infovis03--1284026 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) BARD: a visualization tool for biological sequence analysis. We present BARD (biological arc diagrams), a visualization tool for biological sequence analysis. The development of BARD began with the application of Wattenberg's arc diagrams [Wattenberg 2002] to results from sequence analysis programs, such as BLAST [Altschul et al. 1990]. In this paper, we extend the initial arc diagram concept in two ways: 1) by separating the visualization method from the underlying matching algorithm and 2) by expanding the types of matches to include inexact matches, complemented palindrome matches, and inter-sequence matches. BARD renders each type of match distinctly, resulting in a powerful tool to quickly understand sequence similarities and differences. We illustrate the power of BARD by applying the technique to a comparative sequence analysis of the human pathogenic fungi Cryptococcus neoformans. Brady, R. Dierich, F. Spell, R. InfoVis information visualization 2003 IEEE Symposium on Information Visualization (InfoVis) BARD ard diagram comparative genomics sequence analysis visualization 2003 infovis03--1249031 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Between aesthetics and utility: designing ambient information visualizations. Unlike traditional information visualization, ambient information visualizations reside in the environment of the user rather than on the screen of a desktop computer. Currently, most dynamic information that is displayed in public places consists of text and numbers. We argue that information visualization can be employed to make such dynamic data more useful and appealing. However, visualizations intended for non-desktop spaces will have to both provide valuable information and present an attractive addition to the environment - they must strike a balance between aesthetical appeal and usefulness. To explore this, we designed a real-time visualization of bus departure times and deployed it in a public space, with about 300 potential users. To make the presentation more visually appealing, we took inspiration from a modern abstract artist. The visualization was designed in two passes. First, we did a preliminary version that was presented to and discussed with prospective users. Based on their input, we did a final design. We discuss the lessons learned in designing this and previous ambient information visualizations, including how visual art can be used as a design constraint, and how the choice of information and the placement of the display affect the visualization. Holmquist, L.E. Ljungblad, S. Skog, T. aesthetics text InfoVis aesthetics ambient information visualizations art bus departure times computer displays data visualisation design constraint dynamic information information use information visualization large screen displays modern abstract artist multimedia systems nondesktop spaces real-time visualization valuable information vehicles visual art 2003 IEEE Symposium on Information Visualization (InfoVis) ambient displays ambient information visualization calm technology informative art 2003 infovis03--1249028 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Thread Arcs: an email thread visualization. This paper describes Thread Arcs, a novel interactive visualization technique designed to help people use threads found in email. Thread Arcs combine the chronology of messages with the branching tree structure of a conversational thread in a mixed-model visualization by Venolia and Neustaedter (2003) that is stable and compact. By quickly scanning and interacting with Thread Arcs, people can see various attributes of conversations and find relevant messages in them easily. We tested this technique against other visualization techniques with users' own email in a functional prototype email client. Thread Arcs proved an excellent match for the types of threads found in users' email for the qualities users wanted in small-scale visualizations. Kerr, B. InfoVis Thread Arcs branching tree structure conversational thread data visualisation electronic mail email client email thread visualization graphical user interfaces information visualization interactive visualization message chronology mixed-model visualization tree data structures 2003 IEEE Symposium on Information Visualization (InfoVis) conversations discussions electronic mail email information visualization threads tree structures user interface 2003 infovis03--1249027 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) FundExplorer: supporting the diversification of mutual fund portfolios using context treemaps. An equity mutual fund is a financial instrument that invests in a set of stocks. Any two different funds may partially invest in some of the same stocks, thus overlap is common. Portfolio diversification aims at spreading an investment over many different stocks in search of greater returns. Helping people with portfolio diversification is challenging because it requires informing them about both their current portfolio of stocks held through funds and the other stocks in the market not invested in yet. Current stock/fund visualization systems either waste screen real estate and visualization of all data points. We have developed a system called FundExplorer that implements a distorted treemap to visualize both the amount of money invested in a person's fund portfolio and the context of remaining market stocks. The FundExplorer system enables people to interactively explore diversification possibilities with their portfolios. Csallner, C. Handte, M. Lehmann, O. Stasko, J. financial treemap InfoVis context treemaps data visualisation distorted treemap diversification support equity mutual fund financial data processing financial instrument fund visualization systems investment investment visualization mutual fund portfolios portfolio diversification stock market stock markets stock visualization systems stocks investment tree data structures 2003 IEEE Symposium on Information Visualization (InfoVis) FundExplorer context distortion financial data information visualization query stock market treemap 2003 infovis03--1249026 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Visualization of large-scale customer satisfaction surveys using a parallel coordinate tree. Satisfaction surveys are an important measurement tool in fields such as market research or human resources management. Serious studies consist of numerous questions and contain answers from large population samples. Aggregation on both sides, the questions asked as well as the answers received, turns the multidimensional problem into a complex system of interleaved hierarchies. Traditional ways of presenting the results are limited to one-dimensional charts and cross-tables. We developed a visualization method called the Parallel Coordinate Tree that combines multidimensional analysis with a tree structure representation. Distortion-oriented focus+context techniques are used to facilitate interaction with the visualization. In this paper we present a design study of a commercial application that we built, using this method to analyze and communicate results from large-scale customer satisfaction surveys. Brodbeck, D. Girardin, L. design study distortion focus+context hierarchies interaction InfoVis customer satisfaction customer satisfaction surveys data visualisation distortion-oriented focus+context techniques graphical user interfaces human factors human resources management market research marketing data processing measurement tool multidimensional analysis multidimensional systems parallel coordinate tree parallel programming tree data structures tree structure representation 2003 IEEE Symposium on Information Visualization (InfoVis) focus+context hierarchical data parallel coordinates satisfaction survey 2003 infovis03--1249025 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Causality visualization using animated growing polygons. We present Growing Polygons, a novel visualization technique for the graphical representation of causal relations and information flow in a system of interacting processes. Using this method, individual processes are displayed as partitioned polygons with color-coded segments showing dependencies to other processes. The entire visualization is also animated to communicate the dynamic execution of the system to the user. The results from a comparative user study of the method show that the Growing Polygons technique is significantly more efficient than the traditional Hasse diagram visualization for analysis tasks related to deducing information flow in a system for both small and large executions. Furthermore, our findings indicate that the correctness when solving causality tasks is significantly improved using our method. In addition, the subjective ratings of the users rank the method as superior in all regards, including usability, efficiency, and enjoyability. Elmqvist, N. Tsigas, P. color usability user study InfoVis Hasse diagram visualization animated growing polygons causal relations causality visualization color coded segments computer animation concurrent engineering data visualisation dynamic execution graphical representation graphical user interfaces information flow information visualization interactive animation interactive systems partitioned polygons software engineering subjective user ratings 2003 IEEE Symposium on Information Visualization (InfoVis) causal relations information visualization interactive animation 2003 infovis03--1249023 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Coordinated graph and scatter-plot views for the visual exploration of microarray time-series data. Microarrays are relatively new, high-throughput data acquisition technology for investigating biological phenomena at the micro-level. One of the more common procedures for microarray experimentation is that of the microarray time-course experiment. The product of microarray time-course experiment is time-series data, which subject to proper analysis has the potential to have significant impact on the diagnosis, treatment, and prevention of diseases. While existing information visualization techniques go some way to making microarray time-series data more manageable, requirements analysis has revealed significant limitations. The main finding was that users were unable to uncover and quantify common changes in value over a specified time-period. This paper describes a novel technique that provides this functionality by allowing the user to visually formulate and modify measurable queries with separate time-period and condition components. These visual queries are supported by the combination of a traditional value against time graph representation of the data with a complementary scatter-plot representation of a specified time-period. The multiple views of the visualization are coordinated so that the user can formulate and modify queries with rapid reversible display of query results in the traditional value against time graph format. Craig, P. Kennedy, J. experiment graph multiple views InfoVis bioinformatics biological phenomena investigation data acquisition data visualisation disease diagnosis disease prevention disease treatment graph view information visualization techniques microarray experimentation microarray time-course experiment microarray time-series data microarrays multiple views query formulation requirements analysis scatter-plot view time graph data representation time series user interfaces visual exploration visual queries 2003 IEEE Symposium on Information Visualization (InfoVis) bioinformatics information visualization microarrays multiple views time series 2003 infovis03--1249022 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Conveying shape with texture: an experimental investigation of the impact of texture type on shape categorization judgments. As visualization researchers, we are interested in gaining a better understanding of how to effectively use texture to facilitate shape perception. If we could design the ideal texture pattern to apply to an arbitrary smoothly curving shape to be most accurately and effectively perceived, what would the characteristics of that texture pattern be? In this paper we describe the results of a comprehensive controlled observer experiment intended to yield insight into that question. Here, we report the results of a new study comparing the relative accuracy of observers' judgments of shape type (elliptical, cylindrical, hyperbolic or flat) and shape orientation (convex, concave, both, or neither) for local views of boundary masked quadric surface patches under six different principal direction texture pattern conditions plus two texture conditions (an isotropic pattern and a non-principal direction oriented anisotropic pattern), under both perspective and orthographic projection conditions and from both head-on and oblique viewpoints. Our results confirm the hypothesis that accurate shape perception is facilitated to a statistically significantly greater extent by some principal direction texture patterns than by others. Specifically, we found that, for both views, under conditions of perspective projection, participants more often correctly identified the shape category and the shape orientation when the surface was textured with the pattern that contained oriented energy along both the first and second principal directions only than in the case of any other texture condition. Patterns containing markings following only one of the principal directions, or containing information along other directions in addition to the principal directions yielded poorer performance overall. Hagh-Shenas, H. Interrante, V. Kim, S. experiment insight perception InfoVis arbitrary smoothly curving shape boundary masked quadric surface patches concave controlled observer experiment convex cylindrical shape data visualisation elliptical shape experimental investigation flat shape head-on viewpoint hyperbolic shape image texturean isotropic pattern nonprincipal direction oblique viewpoint observer judgments oriented energy orthographic projection conditions perspective projection principal direction texture pattern conditions shape categorization judgments shape category shape conveyance shape orientation shape perception shape type texture type visualization research 2003 IEEE Symposium on Information Visualization (InfoVis) principal directions shape perception texture 2003 infovis03--1249020 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Empirical comparison of dynamic query sliders and brushing histograms. Dynamic queries facilitate rapid exploration of information by real-time visual display of both query formulation and results. Dynamic query sliders are linked to the main visualization to filter data. A common alternative to dynamic queries is to link several simple visualizations, such as histograms, to the main visualization with a brushing interaction strategy. Selecting data in the histograms highlights that data in the main visualization. We compare these two approaches in an empirical experiment on DataMaps, a geographic data visualization tool. Dynamic query sliders resulted in better performance for simple range tasks, while brushing histograms was better for complex trend evaluation and attribute relation tasks. Participants preferred brushing histograms for understanding relationships between attributes and the rich information they provided. Li, Q. North, C. brushing dynamic query evaluation experiment filter geographic interaction InfoVis DataMaps brushing histograms brushing interaction complex trend evaluation data visualisation dynamic queries dynamic query sliders filter data geographic data visualization graphical user interfaces information exploration information filters information visualization multidimensional visualization query formulation real-time visual display usability study 2003 IEEE Symposium on Information Visualization (InfoVis) dynamic query histogram information visualization multidimensional visualization slider usability study 2003 infovis03--1249019 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Constant density displays using diversity sampling. The Informedia Digital Video Library user interface summarizes query results with a collage of representative keyframes. We present a user study in which keyframe occlusion caused difficulties. To use the screen space most efficiently to display images, both occlusion and wasted whitespace should be minimized. Thus optimal choices will tend toward constant density displays. However, previous constant density algorithms are based on global density, which leads to occlusion and empty space if the density is not uniform. We introduce an algorithm that considers the layout of individual objects and avoids occlusion altogether. Efficiency concerns are important for dynamic summaries of the Informedia Digital Video Library, which has hundreds of thousands of shots. Posting multiple queries that take into account parameters of the visualization as well as the original query reduces the amount of work required. This greedy algorithm is then compared to an optimal one. The approach is also applicable to visualizations containing complex graphical objects other than images, such as text, icons, or trees. Christel, M.G. Derthick, M. Hauptmann, A.G. Wactlar, H.D. occlusion text user study InfoVis Informedia Digital Video Library computer displays constant density algorithms constant density displays data visualisation diversity sampling global density graphical objects graphical user interfaces greedy algorithm hidden feature removal human factors image display information visualization keyframe occlusion query results representative keyframes user interface 2003 IEEE Symposium on Information Visualization (InfoVis) collage information visualization 2003 infovis03--1249018 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Intelligently resolving point occlusion. Large and high-dimensional data sets mapped to low-dimensional visualizations often result in perceptual ambiguities. One such ambiguity is overlap or occlusion that occurs when the number of records exceeds the number of unique locations in the presentation or when there exist two or more records that map to the same location. To lessen the affect of occlusion, non-standard visual attributes (i.e. shading and/or transparency) are applied, or such records may be remapped to a corresponding jittered location. The resulting mapping efficiently portrays the crowding of records but fails to provide the insight into the relationship between the neighboring records. We introduce a new interactive technique that intelligibly organizes overlapped points, a neural network-based smart jittering algorithm. We demonstrate this technique on a scatter plot, the most widely used visualization. The algorithm can be applied to other one, two, and multi-dimensional visualizations which represent data as points, including 3-dimensional scatter plots, RadViz, polar coordinates. Cvek, U. Grinstein, G. Trutschl, M. high-dimensional data insight network occlusion InfoVis RadViz Smart Jittering algorithm data density data points data sets data visualisation data visualization graphical user interfaces hidden feature removal identifiable points information visualization jitter learning (artificial intelligence) low-dimensional visualizations multidimensional visualizations neighboring records neural nets neural network neural networks nonstandard visual attributes point occlusion polar coordinates scatter plot shading transparency 2003 IEEE Symposium on Information Visualization (InfoVis) data density data points data visualization design identifiable points information visualization jitter neural networks occlusion 2003 infovis03--1249016 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Mapping nominal values to numbers for effective visualization. Data sets with a large number of nominal variables, some with high cardinality, are becoming increasingly common and need to be explored. Unfortunately, most existing visual exploration displays are designed to handle numeric variables only. When importing data sets with nominal values into such visualization tools, most solutions to date are rather simplistic. Often, techniques that map nominal values to numbers do not assign order or spacing among the values in a manner that conveys semantic relationships. Moreover, displays designed for nominal variables usually cannot handle high cardinality variables well. This paper addresses the problem of how to display nominal variables in general-purpose visual exploration tools designed for numeric variables. Specifically, we investigate (1) how to assign order and spacing among the nominal values, and (2) how to reduce the number of distinct values to display. We propose that nominal variables be pre-processed using a distance-quantification-classing (DQC) approach before being imported into a visual exploration tool. In the distance step, we identify a set of independent dimensions that can be used to calculate the distance between nominal values. In the quantification step, we use the independent dimensions and the distance information to assign order and spacing among the nominal values. In the classing step, we use results from the previous steps to determine which values within a variable are similar to each other and thus can be grouped together. Each step in the DQC approach can be accomplished by a variety of techniques. We extended the XmdvTool package to incorporate this approach. We evaluated our approach on several data sets using a variety of evaluation measures. Brown, D.C. Rosario, G.E. Rundensteiner, E.A. Ward, M.O. evaluation nominal InfoVis DQC approach XmdvTool package classing step clustering correspondence analysis data compression data sets data visualisation data visualization dimension reduction distance step distance-quantification-classing mathematics computing nominal data nominal value mapping nominal variables norminal values numeric variables quantification step software packages visual exploration displays visualization tools 2003 IEEE Symposium on Information Visualization (InfoVis) classing clustering correspondence analysis dimension reduction nominal data quantification visualization 2003 infovis03--1249015 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. Large number of dimensions not only cause clutter in multi-dimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension management, such as dimension ordering, spacing and filtering, is critical for visual exploration of such datasets. Dimension ordering and spacing explicitly reveal dimension relationships in arrangement-sensitive multidimensional visualization techniques, such as parallel coordinates, star glyphs, and pixel-oriented techniques. They facilitate the visual discovery of patterns within the data. Dimension filtering hides some of the dimensions to reduce clutter while preserving the major information of the dataset. In this paper, we propose an interactive hierarchical dimension ordering, spacing and filtering approach, called DOSFA. DOSFA is based on dimension hierarchies derived from similarities among dimensions. It is scalable multi-resolution approach making dimensional management a tractable task. On the one hand, it automatically generates default settings for dimension ordering, spacing and filtering. On the other hand, it allows users to efficiently control all aspects of this dimension management process via visual interaction tools for dimension hierarchy manipulation. A case study visualizing a dataset containing over 200 dimensions reveals high dimensional visualization techniques. Peng, W. Rundensteiner, E.A. Wang, J. Ward, M.O. case study hierarchies hierarchy interaction parallel coordinates pixel InfoVis DOSFA data mining data space navigation data visualisation dimension filtering dimension hierarchies dimension hierarchy manipulation dimension management dimension ordering dimension spacing dimensional management graphical user interfaces high dimensional dataset exploration high dimensional datasets interactive hierarchical dimensions multidimensional visualization multidimensional visualizations parallel coordinates pattern clustering pixel-oriented techniques star glyphs visual exploration visual interaction tools 2003 IEEE Symposium on Information Visualization (InfoVis) dimension filtering dimension ordering dimension spacing high dimensional datasets multidimensional visualization 2003 infovis03--1249014 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Dynamic visualization of transient data streams. We introduce two dynamic visualization techniques using multidimensional scaling to analyze transient data streams such as newswires and remote sensing imagery. While the time-sensitive nature of these data streams requires immediate attention in many applications, the unpredictable and unbounded characteristics of this information can potentially overwhelm many scaling algorithms that require a full re-computation for every update. We present an adaptive visualization technique based on data stratification to ingest stream information adaptively when influx rate exceeds processing rate. We also describe an incremental visualization technique based on data fusion to project new information directly onto a visualization subspace spanned by the singular vectors of the previously processed neighboring data. The ultimate goal is to leverage the value of legacy and new information and minimize re-processing of the entire dataset in full resolution. We demonstrate these dynamic visualization results using a newswire corpus and a remote sensing imagery sequence. Adams, D. Cowley, W. Foote, H. Thomas, J. Wong, P.C. InfoVis adaptive visualization data fusion data fusion-based visualization data mining data stratification data stratification-based visualization data visualisation dynamic visualization incremental visualization multidimensional scaling neighboring data newswires remote sensing remote sensing imagery sensor fusion singular vectors text visualization transient data streams visualization subspace 2003 IEEE Symposium on Information Visualization (InfoVis) dynamic visualization remote sensing imagery text visualization transient data stream 2003 infovis03--1249010 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Visualizing evolving networks: minimum spanning trees versus pathfinder networks. Network evolution is an ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs). Our primary goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account. Chen, C. Morris, S. animation network InfoVis MST PFNET botulinum toxin research citation analysis cocitation networks complex networks data visualisation dynamical properties evolving network visualization growth animation high-degree nodes link reduction algorithms minimum spanning trees network evolution pathfinder networks scale-free networks scientific publications shortest paths small-world networks solid modelling topological properties trees (mathematics) ubiquitous computing visualization assessment 2003 IEEE Symposium on Information Visualization (InfoVis) co-citation networks minimum spanning trees network evolution network visualization pathfinder networks 2003 infovis03--1249009 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) MoireGraphs: radial focus+context visualization and interaction for graphs with visual nodes. Graph and tree visualization techniques enable interactive exploration of complex relations while communicating topology. However, most existing techniques have not been designed for situations where visual information such as images is also present at each node and must be displayed. This paper presents MoireGraphs to address this need. MoireGraphs combine a new focus+context radial graph layout with a suite of interaction techniques (focus strength changing, radial rotation, level highlighting, secondary foci, animated transitions and node information) to assist in the exploration of graphs with visual nodes. The method is scalable to hundreds of displayed visual nodes. Jankun-Kelly, T.J. Ma, K.-L. focus+context graph graph layout interaction radial InfoVis MoireGraphs animated transitions communicating topology data visualisation graph drawing graphs information visualization level highlighting node information radial focus+context interaction radial focus+context visualization radial graph layout radial rotation tree visualization trees (mathematics) visual information visual nodes 2003 IEEE Symposium on Information Visualization (InfoVis) focus+context graph drawing information visualization radial graph layout 2003 infovis03--1249008 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Edgelens: an interactive method for managing edge congestion in graphs. An increasing number of tasks require people to explore, navigate and search extremely complex data sets visualized as graphs. Examples include electrical and telecommunication networks, Web structures, and airline routes. The problem is that graphs of these real world data sets have many interconnected nodes, ultimately leading to edge congestion: the density of edges is so great that they obscure nodes, individual edges, and even the visual information beneath the graph. To address this problem we developed an interactive technique called EdgeLens. An EdgeLens interactively curves graph edges away for a person's focus attention without changing the node positions. This opens up sufficient space to disambiguate node and edge relationships and to see underlying information while still preserving node layout. Initially two methods of creating this interaction were developed and compared in a user study. The results of this study were used in the selection of a basic approach and the subsequent development of the EdgeLens. We then improved the EdgeLens through use of transparency and colour and by allowing multiple lenses to appear on the graph. Carpendale, S. Greenberg, S. Wong, N. graph interaction user study InfoVis Edgelens Web structures air traffic airline routes data sets data visualisation data visualization distortion lens electrical networks graph edge congestion management graph layout graphs individual edges information visualization interactive method interactive visualization interconnected nodes navigation telecommunication networks visual information 2003 IEEE Symposium on Information Visualization (InfoVis) distortion lens edge congestion graph layout information visualization interactive visualization navigation 2003 infovis03--1249007 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Design choices when architecting visualizations. In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets. The design decisions we discuss in this paper include: the internal data model, data access, semantic meta-data information the visualization can use to create effective visual decodings, the need for data transformations in a visualization tool, modular objects for flexibility, and the tradeoff between simplicity and expressiveness when providing methods for creating visualizations. Bosche, R. Stolte, C. Tang, D. InfoVis Rivet data access data sets data transformations data visualisation design tradeoffs information systems information visualization internal data model meta data modular objects semantic meta-data information software architecture system architecture visual databases visual decodings visualization architecture visualization system visualization tool prototyping 2003 IEEE Symposium on Information Visualization (InfoVis) data transformations design tradeoffs information visualization semantic meta-data system architecture 2003 infovis03--1249006 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Exploring high-D spaces with multiform matrices and small multiples. We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, multiform, bivariate matrix and a complementary multiform, bivariate small multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed form the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors. Guo, D. Hardisty, F. Lengerich, G. MacEachren, A.M. Xiping, D. dynamic query geovisualization high-dimensional data matrix scatterplot small multiples toolkit InfoVis EDA GeoVISTA Studio bivariate maps bivariate matrix bivariate representation bivarite small multiple plot cancer diagnosis component-based architecture conditional entropy data analysis data mining data visualisation default order exploratory data analysis geographic information systems geovisualization high-dimensional data set high-dimensional space exploration information visualization medical diagnostic computing mortality data multiform matrices multiple display multivariate data object-oriented programming pattern recognition risk factors scatterplot scatterplot matrices scatterplot matrix small multiples space-filling displays visual analysis visual databases 2003 IEEE Symposium on Information Visualization (InfoVis) EDA GeoVISTA Studio bivariate map conditional entropy conditioning geovisualization scatterplot matrix small multiples space-filling visualization 2003 infovis03--1249005 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) A model of multi-scale perceptual organization in information graphics. We propose a new method for assessing the perceptual organization of information graphics, based on the premise that the visual structure of an image should match the structure of the data it is intended to convey. The core of our method is a new formal model of one type of perceptual structure, based on classical machine vision techniques for analyzing an image at multiple resolutions. The model takes as input an arbitrary grayscale image and returns a lattice structure describing the visual organization of the image. We show how this model captures several aspects of traditional design aesthetics, and we describe a software tool that implements the model to help designers analyze and refine visual displays. Our emphasis here is on demonstrating the model's potential as a design aid rather than as a description of human perception, but given its initial promise we propose a variety of ways in which the model could be extended and validated. Fisher, D. Wattenberg, M. aesthetics perception InfoVis computer displays computer vision data visualisation design aid design methodology formal model graphical user interfaces grayscale image human perception information graphics lattice structure machine vision multiple resolutions multiscale perceptual organization perceptual structure scale space screen design software psychology software tool software tools user interfaces user/machine systems visual displays visual organization visual structure 2003 IEEE Symposium on Information Visualization (InfoVis) design methodology perceptual organization scale space visualization 2003 infovis03--1249004 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Smooth and efficient zooming and panning. Large 2D information spaces, such as maps, images, or abstract visualizations, require views at various level of detail: close ups to inspect details, overviews to maintain (literally) an overview. Users often switch between these views. We discuss how smooth animations from one view to another can be defined. To this end, a metric on the effect of simultaneous zooming and panning is defined, based on an estimate of the perceived velocity. Optimal is defined as smooth and efficient. Given the metric, these terms can be translated into a computational model, which is used to calculate an analytic solution for optimal animations. The model has two free parameters: animation speed and zoom/pan trade off. A user experiment to find good values for these is described. Nuij, W.A.A. van Wijk, J.J. animation experiment overview zoom zooming InfoVis 2D information spaces abstract visualization animation speed computational geometry computational model computer animation data visualisation graphical user interfaces image processing image visualization inspection techniques map visualization navigation optimal animations panning perceived velocity scale space scrolling smooth animations software engineering user interfaces zooming 2003 IEEE Symposium on Information Visualization (InfoVis) navigation panning scale space scrolling zooming 2003 infovis03--1249013 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) A virtual workspace for hybrid multidimensional scaling algorithms. In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views. Chalmers, M. Ross, G. multiple views InfoVis MDS algorithmic architecture computational complexity data flows data sets data structures data visualisation data volume hybrid algorithm hybrid combinations multidimensional data multidimensional scaling variable distribution variable types virtual workspace visual programming visual programmingHIVE 2003 IEEE Symposium on Information Visualization (InfoVis) complexity data-flow hybrid algorithms multidimensional scaling multiple views visual programming 2003 infovis03--1249021 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) An experimental evaluation of continuous semantic zooming in program. This paper presents the results of an experiment aimed at investigating how different methods of viewing visual programs affect users' understanding. The first two methods used traditional flat and semantic zooming models of program representation; the third is a new representation that uses semantic zooming combined with blending and proximity. The results of several search tasks performed by approximately 80 participants showed that the new method resulted in both faster and more accurate searches than the other methods. Caudell, T.P. Goldsmith, T.E. Kubica, S. Summers, K.L. evaluation experiment zooming InfoVis continuous semantic zooming data visualisation flat zooming graphical user interfaces human subjects testing program representation program visualisation program visualization reverse engineering user understanding visual program languages visual programs 2003 IEEE Symposium on Information Visualization (InfoVis) human subjects testing program visualization visual program languages 2003 infovis03--1249024 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Compound brushing [dynamic data visualization]. This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure/control various common types of brushing techniques currently used in dynamic data visualization, but also to investigate new brushing techniques. Chen, H. brushing InfoVis brushing techniques compound brushing data visualisation device dynamic data visualization dynamic graphics dynamic query graphical user interfaces higraphs renderer selection transformation visual programming visual programming tool 2003 IEEE Symposium on Information Visualization (InfoVis) brushing data visualization dynamic graphics dynamic query higraph selection visual programming 2003 infovis03--1249030 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Using multilevel call matrices in large software projects. Traditionally, node link diagrams are the prime choice when it comes to visualizing software architectures. However, node link diagrams often fall short when used to visualize large graph structures. In this paper we investigate the use of call matrices as visual aids in the management of large software projects. We argue that call matrices have a number of advantages over traditional node link diagrams when the main object of interest is the link instead of the node. Matrix visualizations can provide stable and crisp layouts of large graphs and are inherently well suited for large multilevel visualizations because of their recursive structure. We discuss a number of visualization issues, using a very large software project currently under development at Philips Medical Systems as a running example. van Ham, F. graph matrix InfoVis Philips Medical Systems data visualisation graph structures graphical user interfaces large software projects matrix visualizations medical information systems multilevel call matrices multilevel visualizations node-link diagram program visualisation recursive structure screens (display) software architecture software visualization visual aids 2003 IEEE Symposium on Information Visualization (InfoVis) call matrix multilevel visualization software visualization 2003 infovis03--1249011 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Multiscale visualization of small world networks. Many networks under study in Information Visualization are "small world" networks. These networks first appeared in the study social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics. Auber, D. Chircota, Y. Jourdan, F. Melancon, G. graph hierarchy navigation network social zooming InfoVis 2003 IEEE Symposium on Information Visualization (InfoVis) clustering metric multiscale graphs semantic zooming small world networks 2003 infovis03--1249012 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Improving Hybrid MDS with Pivot-based Searching. An algorithm is presented for the visualisation of multidimensional abstract data, building on a hybrid model introduced at InfoVis 2002. The most computationally complex stage of the original model involved performing a nearest-neighbour search for every data item. The complexity of this phase has been reduced by treating all high-dimensional relationships as a set of discretised distances to a constant number of randomly selected pivot items. In improving this computational bottleneck, the complexity is reduced from O(NsqrtN) to O(N5/4). As well as documenting this improvement, the paper describes evaluation with a data set of 108000 14-dimensional items; a considerable increase on the size of data previously tested. Results illustrate that the reduction in complexity is reflected in significantly improved run times and that no negative impact is made upon the quality of layout produced. Chalmers, M. Morrison, A. evaluation InfoVis 2003 IEEE Symposium on Information Visualization (InfoVis) MDS force directed placement hybrid algorithms multidimensional scaling near-neighbour search pivots spring models 2003 infovis03--1249017 10/19/2003 2003 IEEE Symposium on Information Visualization (InfoVis) Visualization of labeled data using linear transformations. We present a novel family of data-driven linear transformations, aimed at visualizing multivariate data in a low-dimensional space in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise similarities and/or dissimilarities between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their intra-structure. All this make our technique very flexible and powerful, and let us cope with kinds of data that other techniques fail to describe properly. Carmel, L. Koren, Y. clustering InfoVis 2003 IEEE Symposium on Information Visualization (InfoVis) Fisher's linear discriminant analysis classification dimensionality-reduction eigenprojection principal component analysis projection visualization 2003 infovis04--1382900 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Evaluating a System for Interactive Exploration of Large, Hierarchically Structured Document Repositories. The InfoSky visual explorer is a system enabling users to interactively explore large, hierarchically structured document collections. Similar to a real-world telescope, InfoSky employs a planar graphical representation with variable magnification. Documents of similar content are placed close to each other and displayed as stars, while collections of documents at a particular level in the hierarchy are visualised as bounding polygons. Usability testing of an early prototype implementation of InfoSky revealed several design issues which prevented users from fully exploiting the power of the visual metaphor. Evaluation results have been incorporated into an advanced prototype, and another usability test has been conducted. A comparison of test results demonstrates enhanced system performance and points out promising directions for further work. Andrews, K. Granitzer, M. Kienreich, W. Klieber, W. Sabol, V. document evaluation hierarchy usability InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) Voronoi document retrieval force-directed placement hierarchical repositories information management information visualization knowledge management navigation 2004 infovis04--1382901 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Metric-Based Network Exploration and Multiscale Scatterplot. We describe an exploratory technique based on the direct interaction with a 2D modified scatterplot computed from two different metrics calculated over the elements of a network. The scatterplot is transformed into an image by applying standard image processing techniques resulting into blurring effects. Segmentation of the image allow to easily select patches on the image as a way to extract subnetworks. We were inspired by the work of Wattenberg and Fisher [M. Wattenberg et al. (2003)] showing that the blurring process builds into a multiscale perceptual scheme, making this type of interaction intuitive to the user. We explain how the exploration of the network can be guided by the visual analysis of the blurred scatterplot and by its possible interpretations. Chiricota, Y. Jourdan, F. Melancon, G. interaction metrics network scatterplot InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) blurring clustering exploration filtering graph navigation multiscale perceptual organization scatterplot 2004 infovis04--1382902 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations. The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging the analytic gap, propose a framework for design and evaluation of information visualization systems, and demonstrate its use. Amar, R. Stasko, J. evaluation InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) analytic gap evaluation framework information visualization knowledge tasks theory 2004 infovis04--1382903 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Rethinking Visualization: A High-Level Taxonomy. We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas. Möller, T. Tory, M. taxonomy InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) classification conceptual model design model taxonomy user model visualization 2004 infovis04--1382904 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Building Highly-Coordinated Visualizations in Improvise. Improvise is a fully-implemented system in which users build and browse multiview visualizations interactively using a simple shared-object coordination mechanism coupled with a flexible, expression-based visual abstraction language. By coupling visual abstraction with coordination, users gain precise control over how navigation and selection in the visualization affects the appearance of data in individual views. As a result, it is practical to build visualizations with more views and richer coordination in Improvise than in other visualization systems. Building and browsing activities are integrated in a single, live user interface that lets users alter visualizations quickly and incrementally during data exploration. Weaver, C. navigation InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) coordinated queries coordination exploratory visualization multiple views visual abstraction language 2004 infovis04--1382905 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) The InfoVis Toolkit. This article presents the InfoVis toolkit, designed to support the creation, extension and integration of advanced 2D information visualization components into interactive Java swing applications. The InfoVis toolkit provides specific data structures to achieve a fast action/feedback loop required by dynamic queries. It comes with a large set of components such as range sliders and tailored control panels required to control and configure the visualizations. These components are integrated into a coherent framework that simplifies the management of rich data structures and the design and extension of visualizations. Supported data structures currently include tables, trees and graphs. Supported visualizations include scatter plots, time series, parallel coordinates, treemaps, icicle trees, node-link diagrams for trees and graphs and adjacency matrices for graphs. All visualizations can use fisheye lenses and dynamic labeling. The InfoVis toolkit supports hardware acceleration when available through Agile2D, an implementation of the Java graphics API based on OpenGL, achieving speedups of 10 to 200 times. The article also shows how new visualizations can be added and extended to become components, enriching visualizations as well as general applications. Fekete, J.-D. fisheye hardware parallel coordinates time series toolkit InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) graphics information visualization integration toolkit 2004 infovis04--1382906 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Topological Fisheye Views for Visualizing Large Graphs. Graph drawing is a basic visualization tool. For graphs of up to hundreds of nodes and edges, there are many effective techniques available. At greater scale, data density and occlusion problems often negate its effectiveness. Conventional pan-and-zoom, and multiscale and geometric fisheye views are not fully satisfactory solutions to this problem. As an alternative, we describe a topological zooming method. It is based on the precomputation of a hierarchy of coarsened graphs, which are combined on the fly into renderings with the level of detail dependent on the distance from one or more foci. We also discuss a related distortion method that allows our technique to achieve constant information density displays. Gansner, E. Koren, Y. North, S.C. distortion fisheye graph graph drawing hierarchy occlusion zoom zooming InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) large graph visualization topological fisheye 2004 infovis04--1382907 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Matrix Zoom: A Visual Interface to Semi-External Graphs. In Web data, telecommunications traffic and in epidemiological studies, dense subgraphs correspond to subsets of subjects (i.e. users, patients) that share a collection of attributes values (i.e. accessed Web pages, email-calling patterns or disease diagnostic profiles). Visual and computational identification of these "clusters" becomes useful when domain experts desire to determine those factors of major influence in the formation of access and communication clusters or in the detection and contention of disease spread. With the current increases in graphic hardware capabilities and RAM sizes, it is more useful to relate graph sizes to the available screen real estate S and the amount of available RAM M, instead of the number of edges or nodes in the graph. We offer a visual interface that is parameterized by M and S and is particularly suited for navigation tasks that require the identification of subgraphs whose edge density is above certain threshold. This is achieved by providing a zoomable matrix view of the underlying data. This view is strongly coupled to a hierarchical view of the essential information elements present in the data domain. We illustrate the applicability of this work to the visual navigation of cancer incidence data and to an aggregated sample of phone call traffic. Abello, J. van Ham, F. graph hardware matrix navigation zoom InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) cancer data clustering external memory algorithms graph visualization hierarchy trees phone traffic 2004 infovis04--1382908 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Dynamic Drawing of Clustered Graphs. This paper presents an algorithm for drawing a sequence of graphs that contain an inherent grouping of their vertex set into clusters. It differs from previous work on dynamic graph drawing in the emphasis that is put on maintaining the clustered structure of the graph during incremental layout. The algorithm works online and allows arbitrary modifications to the graph. It is generic and can be implemented using a wide range of static force-directed graph layout tools. The paper introduces several metrics for measuring layout quality of dynamic clustered graphs. The performance of our algorithm is analyzed using these metrics. The algorithm has been successfully applied to visualizing mobile object software. Frishman, Y. Tal, A. graph graph drawing graph layout metrics InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) dynamic layout graph drawing mobile objects software visualization 2004 infovis04--1382909 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Interactive Visualization of Small World Graphs. Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example. van Ham, F. van Wijk, J.J. cluster clustering database overview social InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) clustering graph drawing graph visualization small world graphs 2004 infovis04--1382910 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Non-Euclidean Spring Embedders. We present a method by which force-directed algorithms for graph layouts can be generalized to calculate the layout of a graph in an arbitrary Riemannian geometry. The method relies on extending the Euclidean notions of distance, angle, and force-interactions to smooth nonEuclidean geometries via projections to and from appropriately chosen tangent spaces. In particular, we formally describe the calculations needed to extend such algorithms to hyperbolic and spherical geometries. Kobourov, S. Wampler, K. graph InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) force-directed algorithms graph drawing hyperbolic space information visualization non-Euclidean geometry spherical space spring embedder 2004 infovis04--1382884 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) An Evaluation of Microarray Visualization Tools for Biological Insight. High-throughput experiments such as gene expression microarrays in the life sciences result in large datasets. In response, a wide variety of visualization tools have been created to facilitate data analysis. Biologists often face a dilemma in choosing the best tool for their situation. The tool that works best for one biologist may not work well for another due to differences in the type of insight they seek from their data. A primary purpose of a visualization tool is to provide domain-relevant insight into the data. Ideally, any user wants maximum information in the least possible time. In this paper we identify several distinct characteristics of insight that enable us to recognize and quantify it. Based on this, we empirically evaluate five popular microarray visualization tools. Our conclusions can guide biologists in selecting the best tool for their data, and computer scientists in developing and evaluating visualizations. Duca, K. North, C. Saraiya, P. evaluation insight InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) bioinformatics data visualization empirical evaluation high throughput experiments insight microarray data 2004 infovis04--1382885 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) User Experiments with Tree Visualization Systems. This paper describes a comparative experiment with five well-known tree visualization systems, and Windows Explorer as a baseline system. Subjects performed tasks relating to the structure of a directory hierarchy, and to attributes of files and directories. Task completion times, correctness and user satisfaction were measured, and video recordings of subjects' interaction with the systems were made. Significant system and task type effects and an interaction between system and task type were found. Qualitative analyses of the video recordings were thereupon conducted to determine reasons for the observed differences, resulting in several findings and design recommendations as well as implications for future experiments with tree visualization systems. Kobsa, A. experiment hierarchy interaction InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) accuracy design recommendations experimental comparison information visualization task performance user interaction user satisfaction 2004 infovis04--1382886 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations. In this paper, we describe a taxonomy of generic graph related tasks and an evaluation aiming at assessing the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation bears on seven generic tasks and leads to important recommendations with regard to the representation of graphs according to their size and density. For instance, we show that when graphs are bigger than twenty vertices, the matrix-based visualization performs better than node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation. Castagliola, P. Fekete, J.-D. Ghoniem, M. evaluation graph matrix taxonomy InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) adjacency matrices evaluation node-link representation readability visualization of graphs 2004 infovis04--1382887 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) GeoTime Information Visualization. Analyzing observations over time and geography is a common task but typically requires multiple, separate tools. The objective of our research has been to develop a method to visualize, and work with, the spatial interconnectedness of information over time and geography within a single, highly interactive 3D view. A novel visualization technique for displaying and tracking events, objects and activities within a combined temporal and geospatial display has been developed. This technique has been implemented as a demonstratable prototype called GeoTime in order to determine potential utility. Initial evaluations have been with military users. However, we believe the concept is applicable to a variety of government and business analysis tasks. Kapler, T. Wright, W. business geospatial InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) 3D visualization geospatial interactive visualization link analysis spatiotemporal visual data analysis 2004 infovis04--1382888 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) RecMap: Rectangular Map Approximations. In many application domains, data is collected and referenced by its geospatial location. Nowadays, different kinds of maps are used to emphasize the spatial distribution of one or more geospatial attributes. The nature of geospatial statistical data is the highly nonuniform distribution in the real world data sets. This has several impacts on the resulting map visualizations. Classical area maps tend to highlight patterns in large areas, which may, however, be of low importance. Cartographers and geographers used cartograms or value-by-area maps to address this problem long before computers were available. Although many automatic techniques have been developed, most of the value-by-area cartograms are generated manually via human interaction. In this paper, we propose a novel visualization technique for geospatial data sets called RecMap. Our technique approximates a rectangular partition of the (rectangular) display area into a number of map regions preserving important geospatial constraints. It is a fully automatic technique with explicit user control over all exploration constraints within the exploration process. Experiments show that our technique produces visualizations of geospatial data sets, which enhance the discovery of global and local correlations, and demonstrate its performance in a variety of applications. Heilmann, R. Keim, D.A. Panse, C. Sips, M. geospatial interaction InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) database and data mining visualization geographic visualization information visualization 2004 infovis04--1382889 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) EZEL: a Visual Tool for Performance Assessment of Peer-to-Peer File-Sharing Network. In this paper we present EZEL, a visual tool we developed for the performance assessment of peer-to-peer file-sharing networks. We start by identifying the relevant data transferred in this kind of networks and the main performance assessment questions. Then we describe the visualization of data from two different points of view. First we take servers as focal points and we introduce a new technique, faded cushioning, which allows visualizing the same data from different perspectives. Secondly, we present the viewpoint of files, and we expose the correlations with the server stance via a special scatter plot. Finally, we discuss how our tool, based on the described techniques, is effective in the performance assessment of peer-to-peer file-sharing networks. Telea, A.C. Voinea, L. van Wijk, J.J. network InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) P2P file-sharing networks visualization distributed file systems visualization process visualization small displays 2004 infovis04--1382890 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) A History Mechanism for Visual Data Mining. A major challenge of current visualization and visual data mining (VDM) frameworks is to support users in the orientation in complex visual mining scenarios. An important aspect to increase user support and user orientation is to use a history mechanism that, first of all, provides un- and redoing functionality. In this paper, we present a new approach to include such history functionality into a VDM framework. Therefore, we introduce the theoretical background, outline design and implementation aspects of a history management unit, and conclude with a discussion showing the usefulness of our history management in a VDM framework. Kreuseler, M. Nocke, T. Schumann, H. data mining history InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) history undo/redo visual data mining visualization 2004 infovis04--1382891 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Steerable, Progressive Multidimensional Scaling. Current implementations of multidimensional scaling (MDS), an approach that attempts to best represent data point similarity in a low-dimensional representation, are not suited for many of today's large-scale datasets. We propose an extension to the spring model approach that allows the user to interactively explore datasets that are far beyond the scale of previous implementations of MDS. We present MDSteer, a steerable MDS computation engine and visualization tool that progressively computes an MDS layout and handles datasets of over one million points. Our technique employs hierarchical data structures and progressive layouts to allow the user to steer the computation of the algorithm to the interesting areas of the dataset. The algorithm iteratively alternates between a layout stage in which a subselection of points are added to the set of active points affected by the MDS iteration, and a binning stage which increases the depth of the bin hierarchy and organizes the currently unplaced points into separate spatial regions. This binning strategy allows the user to select onscreen regions of the layout to focus the MDS computation into the areas of the dataset that are assigned to the selected bins. We show both real and common synthetic benchmark datasets with dimensionalities ranging from 3 to 300 and cardinalities of over one million points. Munzner, T. Williams, M. hierarchy InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) dimensionality reduction multidimensional scaling 2004 infovis04--1382892 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections. Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features. Seo, J. Shneiderman, B. color dynamic query matrix overview scatterplot InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) dynamic query exploratory data analysis feature detection/selection information visualization statistical graphics 2004 infovis04--1382893 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Value and Relation Display for Interactive Exploration of High Dimensional Datasets. Traditional multidimensional visualization techniques, such as glyphs, parallel coordinates and scatterplot matrices, suffer from clutter at the display level and difficult user navigation among dimensions when visualizing high dimensional datasets. In this paper, we propose a new multidimensional visualization technique named a value and relation (VaR) display, together with a rich set of navigation and selection tools, for interactive exploration of datasets with up to hundreds of dimensions. By explicitly conveying the relationships among the dimensions of a high dimensional dataset, the VaR display helps users grasp the associations among dimensions. By using pixel-oriented techniques to present values of the data items in a condensed manner, the VaR display reveals data patterns in the dataset using as little screen space as possible. The navigation and selection tools enable users to interactively reduce clutter, navigate within the dimension space, and examine data value details within context effectively and efficiently. The VaR display scales well to datasets with large numbers of data items by employing sampling and texture mapping. A case study on a real dataset, as well as the VaR displays of multiple real datasets throughout the paper, reveals how our proposed approach helps users interactively explore high dimensional datasets with large numbers of data items. Huang, S. Mehta, N. Patro, A. Rundensteiner, E.A. Ward, M.O. Yang, J. case study navigation parallel coordinates pixel scatterplot InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) high dimensional datasets multi-dimensional scaling multi-dimensional visualization pixel-oriented 2004 infovis04--1382894 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Uncovering Clusters in Crowded Parallel Coordinates Visualizations. The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the user's ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in parallel coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets. Artero, A.O. Levkowitz, H. de Oliveira, M.C.F. filter interaction parallel coordinates InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) density-based visualization information visualization visual clustering visual data mining 2004 infovis04--1382895 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering. Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display. Peng, W. Rundensteiner, E.A. Ward, M.O. InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) dimension order multidimensional visualization visual clutter visual structure 2004 infovis04--1382896 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Time-Varying Data Visualization Using Information Flocking Boids. This research demonstrates how principles of self-organization and behavior simulation can be used to represent dynamic data evolutions by extending the concept of information flocking, originally introduced by Proctor & Winter (1998), to time-varying datasets. A rule-based behavior system continuously controls and updates the dynamic actions of individual, three-dimensional elements that represent the changing data values of reoccurring data objects. As a result, different distinguishable motion types emerge that are driven by local interactions between the spatial elements as well as the evolution of time-varying data values. Notably, this representation technique focuses on the representation of dynamic data alteration characteristics, or how reoccurring data objects change over time, instead of depicting the exact data values themselves. In addition, it demonstrates the potential of motion as a useful information visualization cue. The original information flocking approach is extended to incorporate time-varying datasets, live database querying, continuous data streaming, real-time data similarity evaluation, automatic shape generation and more stable flocking algorithms. Different experiments prove that information flocking is capable of representing short-term events as well as long-term temporal data evolutions of both individual and groups of time-dependent data objects. An historical stock market quote price dataset is used to demonstrate the algorithms and principles of time-varying information flocking. Moere, A.V. database evaluation InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) 3D information visualization artificial life boids motion time-varying information visualization 2004 infovis04--1382897 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Artifacts of the Presence Era: Using Information Visualization to Create an Evocative Souvenir. We present Artifacts of the Presence Era, a digital installation that uses a geological metaphor to visualize the events in a physical space over time. The piece captures video and audio from a museum and constructs an impressionistic visualization of the evolving history in the space. Instead of creating a visualization tool for data analysis, we chose to produce a piece that functions as a souvenir of a particular time and place. We describe the design choices we made in creating this installation, the visualization techniques we developed, and the reactions we observed from users and the media. We suggest that the same approach can be applied to a more general set of visualization contexts, ranging from email archives to newsgroups conversations. Donath, J. Howe, E. Perry, E. ViĂ©gas, F.B. history InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) history public space visualization 2004 infovis04--1382898 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Paint Inspired Color Mixing and Compositing for Visualization. Color is often used to convey information, and color compositing is often required while visualizing multiattribute information. This paper proposes an alternative method for color compositing. In order to present understandable color blending to the general public, several techniques are proposed. First, a paint-inspired RYB color space is used. In addition, noise patterns are employed to produce subregions of pure color within an overlapped region. We show examples to demonstrate the effectiveness of our technique for visualization. Chen, B. Gossett, N. color InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) RYB color mixing perception 2004 infovis04--1382899 10/10/2004 2004 IEEE Symposium on Information Visualization (InfoVis) Expand-Ahead: A Space-Filling Strategy for Browsing Trees. Many tree browsers allow subtrees under a node to be collapsed or expanded, enabling the user to control screen space usage and selectively drill-down. However, explicit expansion of nodes can be tedious. Expand-ahead is a space-filling strategy by which some nodes are automatically expanded to fill available screen space, without expanding so far that nodes are shown at a reduced size or outside the viewport. This often allows a user exploring the tree to see further down the tree without the effort required in a traditional browser. It also means the user can sometimes drill-down a path faster, by skipping over levels of the tree that are automatically expanded for them. Expand-ahead differs from many detail-in-context techniques in that there is no scaling or distortion involved. We present 1D and 2D prototype implementations of expand-ahead, and identify various design issues and possible enhancements to our designs. Our prototypes support smooth, animated transitions between different views of a tree. We also present the results of a controlled experiment which show that, under certain conditions, users are able to drill-down faster with expand-ahead than without. Balakrishnan, R. Davison, G. McGuffin, M.J. distortion experiment InfoVis 2004 IEEE Symposium on Information Visualization (InfoVis) adaptive user interface automatic expansion expandahead focus+context space filling tree browsing and navigation 2004 infovis05--1532122 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Baby names, visualization, and social data analysis. The Name Voyager, a Web based visualization of historical trends in baby naming, has proven remarkably popular. This paper discusses the interaction techniques it uses for smooth visual exploration of thousands of time series. We also describe design decisions behind the application and lessons learned in creating an application that makes do-it-yourself data mining popular. The prime lesson, it is hypothesized, is that an information visualization tool may be fruitfully viewed not as a tool but as part of an online social environment. In other words, to design a successful exploratory data analysis tool, one good strategy is to create a system that enables "social" data analysis. Wattenberg, M. data mining interaction social time series InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) 2005) infovis05--1532123 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) A sky dome visualisation for identification of astronomical orientations. It has long been known that ancient temples were frequently oriented along the cardinal directions or to certain points along the horizon where Sun or Moon rise or set on special days of the year. In the last decades, archaeologists have found evidence of even older building structures buried in the soil, with doorways that also appear to have distinct orientations. This paper presents a novel diagram combining archaeological maps with a folded-apart, flattened view of the whole sky, showing the local horizon and the daily paths of Sun, Moon and brighter stars. By use of this diagram, interesting groupings of astronomical orientation directions, e.g. to certain Sunrise and Sunset points could be identified, which were evidently used to mark certain days of the year. Orientations to a few significant stars very likely indicated the beginning of the agricultural year in the middle neolithic period. Gröller, M.E. Zotti, G. InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) archaeology) astronomy data visualization 2005 infovis05--1532124 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Interactive visualization of genealogical graphs. The general problem of visualizing "family trees", or genealogical graphs, in 2D, is considered. A graph theoretic analysis is given, which identifies why genealogical graphs can be difficult to draw. This motivates some novel graphical representations, including one based on a dual tree, a subgraph formed by the union of two trees. Dual trees can be drawn in various styles, including an indented outline style, and allow users to browse general multitrees in addition to genealogical graphs, by transitioning between different dual tree views. A software prototype for such browsing is described, that supports smoothly animated transitions, automatic camera framing, rotation of subtrees, and a novel interaction technique for expanding or collapsing subtrees to any depth with a single mouse drag. Balakrishnan, R. McGuffin, M.J. graph interaction InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) family trees) genealogies genealogy graph browsing and navigation graph drawing graph theory kinship multitrees 2005 infovis05--1532125 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) The visual code navigator: an interactive toolset for source code investigation. We present the Visual Code Navigator, a set of three interrelated visual tools that we developed for exploring large source code software projects from three different perspectives, or views: the syntactic view shows the syntactic constructs in the source code. The symbol view shows the objects a file makes available after compilation, such as function signatures, variables, and namespaces. The evolution view looks at different versions in a project lifetime of a number of selected source files. The views share one code model, which combines hierarchical syntax based and line based information from multiple source files versions. We render this code model using a visual model that extends the pixel-filling, space partitioning properties of shaded cushion treemaps with novel techniques. We discuss how our views allow users to interactively answer complex questions on various code elements by simple mouse clicks. We validate the efficiency and effectiveness of our toolset by an informal user study on the source code of VTK, a large, industry-size C++ code base. Lommerse, G. Nossin, F. Telea, A.C. Voinea, L. pixel user study InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) multiple views) pixel-filling displays source code analysis source code visualization treemap 2005 infovis05--1532126 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Vizster: visualizing online social networks. Recent years have witnessed the dramatic popularity of online social networking services, in which millions of members publicly articulate mutual "friendship" relations. Guided by ethnographic research of these online communities, we have designed and implemented a visualization system for playful end-user exploration and navigation of large scale online social networks. Our design builds upon familiar node link network layouts to contribute customized techniques for exploring connectivity in large graph structures, supporting visual search and analysis, and automatically identifying and visualizing community structures. Both public installation and controlled studies of the system provide evidence of the system's usability, capacity for facilitating discovery, and potential for fun and engaged social activity. Boyd, D. Heer, J. graph navigation network social usability InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) community) data mining exploration graphs play social networks visualization 2005 infovis05--1532127 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) PRISAD: a partitioned rendering infrastructure for scalable accordion drawing. We present PRISAD, the first generic rendering infrastructure for information visualization applications that use the accordion drawing technique: rubber sheet navigation with guaranteed visibility for marked areas of interest. Our new rendering algorithms are based on the partitioning of screen space, which allows us to handle dense dataset regions correctly. The algorithms in previous work led to incorrect visual representations because of overculling, and to inefficiencies due to overdrawing multiple items in the same region. Our pixel based drawing infrastructure guarantees correctness by eliminating overculling, and improves rendering performance with tight bounds on overdrawing. PRITree and PRISeq are applications built on PRISAD, with the feature sets of TreeJuxtaposer and SequenceJuxtaposer, respectively. We describe our PRITree and PRISeq dataset traversal algorithms, which are used for efficient rendering, culling, and layout of datasets within the PRISAD framework. We also discuss PRITree node marking techniques, which offer order-of-magnitude improvements to both memory and time performance versus previous range storage and retrieval techniques. Our PRITree implementation features a five fold increase in rendering speed for nontrivial tree structures, and also reduces memory requirements in some real world datasets by up to eight times, so we are able to handle trees of several million nodes. PRISeq renders fifteen times faster and handles datasets twenty times larger than previous work. Hildebrand, K. Munzner, T. Slack, J. navigation pixel InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) focus+context) information visualization progressive rendering real time rendering 2005 infovis05--1532128 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Voronoi treemaps. Treemaps are a well known method for the visualization of attributed hierarchical data. Previously proposed treemap layout algorithms are limited to rectangular shapes, which cause problems with the aspect ratio of the rectangles as well as with identifying the visualized hierarchical structure. The approach of Voronoi treemaps presented in this paper eliminates these problems through enabling subdivisions of and in polygons. Additionally, this allows for creating treemap visualizations within areas of arbitrary shape, such as triangles and circles, thereby enabling a more flexible adaptation of treemaps for a wider range of applications. Balzer, M. Deussen, O. treemap InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) Voronoi tessellations) Voronoi treemaps hierarchies information visualization treemap trees 2005 infovis05--1532129 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Elastic hierarchies: combining treemaps and node-link diagrams. We investigate the use of elastic hierarchies for representing trees, where a single graphical depiction uses a hybrid mixture, or "interleaving", of more basic forms at different nodes of the tree. In particular, we explore combinations of node link and treemap forms, to combine the space efficiency of treemaps with the structural clarity of node link diagrams. A taxonomy is developed to characterize the design space of such hybrid combinations. A software prototype is described, which we used to explore various techniques for visualizing, browsing and interacting with elastic hierarchies, such as side by side overview and detail views, highlighting and rubber banding across views, visualization of multiple foci, and smooth animations across transitions. The paper concludes with a discussion of the characteristics of elastic hierarchies and suggestions for research on their properties and uses. Chignell, M.H. McGuffin, M.J. Zhao, S. hierarchies overview taxonomy treemap InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) combinations) elastic hierarchies hybrids interaction techniques interactive visualization multiple views node-link diagram overview+detail treemap trees 2005 infovis05--1532130 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Dig-CoLa: directed graph layout through constrained energy minimization. We describe a new method for visualization of directed graphs. The method combines constraint programming techniques with a high performance force directed placement (FDP) algorithm so that the directed nature of the graph is highlighted while useful properties of FDP - such as emphasis of symmetries and preservation of proximity relations - are retained. Our algorithm automatically identifies those parts of the digraph that contain hierarchical information and draws them accordingly. Additionally, those parts that do not contain hierarchy are drawn at the same quality expected from a nonhierarchical, undirected layout algorithm. An interesting application of our algorithm is directional multidimensional scaling (DMDS). DMDS deals with low dimensional embedding of multivariate data where we want to emphasize the overall flow in the data (e.g. chronological progress) along one of the axes. Dwyer, T. Koren, Y. graph graph layout hierarchy InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) 2005) infovis05--1532131 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Dynamic visualization of graphs with extended labels. The paper describes a novel technique to visualize graphs with extended node and link labels. The lengths of these labels range from a short phrase to a full sentence to an entire paragraph and beyond. Our solution is different from all the existing approaches that almost always rely on intensive computational effort to optimize the label placement problem. Instead, we share the visualization resources with the graph and present the label information in static, interactive, and dynamic modes without the requirement for tackling the intractability issues. This allows us to reallocate the computational resources for dynamic presentation of real time information. The paper includes a user study to evaluate the effectiveness and efficiency of the visualization technique. Eagan, J. Foote, H. Mackey, P. Perrine, K. Thomas, J. Wong, P.C. graph user study InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) dynamic animation) graph label placement graph visualization information visualization 2005 infovis05--1532132 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) An evaluation of content browsing techniques for hierarchical space-filling visualizations. Space-filling visualizations, such as the TreeMap, are well suited for displaying the properties of nodes in hierarchies. To browse the contents of the hierarchy, the primary mode of interaction is by drilling down through many successive layers. In this paper we introduce a distortion algorithm based on fisheye and continuous zooming techniques for browsing data in the TreeMap representation. The motivation behind the distortion approach is for assisting users to rapidly browse information displayed in the TreeMap without opening successive layers of the hierarchy. Two experiments were conducted to evaluate the new approach. In the first experiment (N=20) the distortion approach is compared to the drill down method. Results show that subjects are quicker and more accurate in locating targets of interest using the distortion method. The second experiment (N=12) evaluates the effectiveness of the two approaches in a task requiring context, we define as the context browsing task. The results show that subjects are quicker and more accurate in locating targets with the distortion technique in the context browsing task. Irani, P.P. Li, B. Shi, K. distortion evaluation experiment fisheye hierarchies hierarchy interaction treemap zooming InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) browsing) distortion drill-down focus+context hierarchy navigation semantic zooming space-filling visualization treemap 2005 infovis05--1532133 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Turning the bucket of text into a pipe. Many visual analysis tools operate on a fixed set of data. However, professional information analysts follow issues over a period of time and need to be able to easily add new documents to an ongoing exploration. Some analysts handle documents in a moving window of time, with new documents constantly added and old ones aging out. This paper describes both the user interaction and the technical implementation approach for a visual analysis system designed to support constantly evolving text collections. Crow, V. Hetzler, E. Payne, D.A. Turner, A.E. interaction text InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) dynamic visualization) information visualization real-time updating user interaction design 2005 infovis05--1532134 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Visual correlation for situational awareness. We present a novel visual correlation paradigm for situational awareness (SA) and suggest its usage in a diverse set of applications that require a high level of SA. Our approach is based on a concise and scalable representation, which leads to a flexible visualization tool that is both clear and intuitive to use. Situational awareness is the continuous extraction of environmental information, its integration with previous knowledge to form a coherent mental picture, and the use of that picture in anticipating future events. In this paper we build on our previous work on visualization for network intrusion detection and show how that approach can be generalized to encompass a much broader class of SA systems. We first propose a generalization that is based on what we term, the w3 premise, namely that each event must have at least the what, when and where attributes. We also present a second generalization, which increases flexibility and facilitates complex visual correlations. Finally, we demonstrate the generality of our approaches by applying our visualization paradigm in a collection of diverse SA areas. Agutter, J. Foresti, S. Livnat, Y. Moon, S. awareness network InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) network intrusion) situation awareness visualization 2005 infovis05--1532135 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Highlighting conflict dynamics in event data. We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time stamped events, and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events, and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict. Brandes, U. Fleischer, D. Lerner, J. animation overview InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) event analysis) information visualization text mining time-dependent visualization 2005 infovis05--1532136 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Low-level components of analytic activity in information visualization. Existing system level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low level analysis tasks that largely capture people's activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers. Amar, R. Eagan, J. Stasko, J. InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) analytic activity) design evaluation knowledge discovery taxonomy 2005 infovis05--1532137 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Simple 3D glyphs for spatial multivariate data. We present an effort to evaluate the possible utility of a new type of 3D glyphs intended for visualizations of multivariate spatial data. They are based on results from vision research suggesting that our perception of metric 3D structure is distorted and imprecise relative to the actual scene before us (e.g., "metric 3D structure in visualizations" by M. Lind et al. (2003)); only a class of qualitative properties of the scene is perceived with accuracy. These properties are best characterized as being invariant over affine but not Euclidean transformations. They are related, but not identical to, the non-accidental properties (NAPs) described by Lowe in "perceptual organization and visual recognition" (1984) on which the notion of geons is based in "recognition by components - a theory of image understanding" by I. Biederman (1987). A large number of possible 3D glyphs for the visualization of spatial data can be constructed using such properties. One group is based on the local sign of surface curvature. We investigated these properties in a visualization experiment. The results are promising and the implications for visualization are discussed. Forsell, C. Lind, M. Seipel, S. experiment perception theory InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) 3D glyphs) multidimensional visualization perception 2005 infovis05--1532138 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Revealing structure within clustered parallel coordinates displays. In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent tool for visualizing this type of data but has its limitations. This paper deals with one of its main limitations - how to visualize a large number of data items without hiding the inherent structure they constitute. We solve this problem by constructing clusters and using high precision textures to represent them. We also use transfer functions that operate on the high precision textures in order to highlight different aspects of the cluster characteristics. Providing predefined transfer functions as well as the support to draw customized transfer functions makes it possible to extract different aspects of the data. We also show how feature animation can be used as guidance when simultaneously analysing several clusters. This technique makes it possible to visually represent statistical information about clusters and thus guides the user, making the analysis process more efficient. Cooper, M. Jern, M. Johansson, J. Ljung, P. animation cluster insight parallel coordinates InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) clustering) feature animation parallel coordinates transfer function 2005 infovis05--1532139 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Parallel sets: visual analysis of categorical data. The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical dimensions. Only few methods deal properly with such data, mostly because of the discrete nature of categorical data, which does not translate well into the continuous domains of space and color. Parallel sets is a new visualization method that adopts the layout of parallel coordinates, but substitutes the individual data points by a frequency based representation. This abstracted view, combined with a set of carefully designed interactions, supports visual data analysis of large and complex data sets. The technique allows efficient work with meta data, which is particularly important when dealing with categorical datasets. By creating new dimensions from existing ones, for example, the user can filter the data according to his or her current needs. We also present the results from an interactive analysis of CRM data using parallel sets. We demonstrate how the flexible layout eases the process of knowledge crystallization, especially when combined with a sophisticated interaction scheme. Bendix, F. Hauser, H. Kosara, R. categorical color filter interaction parallel coordinates InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) categorical data) interaction meta information 2005 infovis05--1532140 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Multivariate glyphs for multi-object clusters. Aggregating items can simplify the display of huge quantities of data values at the cost of losing information about the attribute values of the individual items. We propose a distribution glyph, in both two- and three-dimensional forms, which specifically addresses the concept of how the aggregated data is distributed over the possible range of values. It is capable of displaying distribution, variability and extent information for up to four attributes at a time of multivariate, clustered data. User studies validate the concept, showing that both glyphs are just as good as raw data and the 3D glyph is better for answering some questions. Chlan, E.B. Rheingans, P. glyph InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) aggregated data) distribution information visualization multivariate visualization 2005 infovis05--1532141 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) An interactive 3D integration of parallel coordinates and star glyphs. Parallel coordinates are a powerful method for visualizing multidimensional data but, when applied to large data sets, they become cluttered and difficult to read. Star glyphs, on the other hand, can be used to display either the attributes of a data item or the values across all items for a single attribute. Star glyphs may readily provide a quick impression; however, since the full data set require multiple glyphs, overall readings are more difficult. We present parallel glyphs, an interactive integration of the visual representations of parallel coordinates and star glyphs that utilizes the advantages of both representations to offset the disadvantages they have separately. We discuss the role of uniform and stepped colour scales in the visual comparison of non-adjacent items and star glyphs. Parallel glyphs provide capabilities for focus-in-context exploration using two types of lenses and interactions specific to the 3D space. Carpendale, S. Fanea, E. Isenberg, T. parallel coordinates InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) 3D visualization) multi-dimensional data sets parallel coordinates parallel glyphs star glyphs 2005 infovis05--1532142 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Graph-theoretic scagnostics. We introduce Tukey and Tukey scagnostics and develop graph-theoretic methods for implementing their procedure on large datasets. Anand, A. Grossman, R. Wilkinson, L. graph InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) statistical graphics) visualization 2005 infovis05--1532143 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Visualizing coordination in situ. Exploratory visualization environments allow users to build and browse coordinated multiview visualizations interactively. As the number of views and amount of coordination increases, conceptualizing coordination structure becomes more and more important for successful data exploration. Integrated metavisualization is exploratory visualization of coordination and other interactive structure directly inside a visualization's own user interface. This paper presents a model of integrated metavisualization, describes the problem of capturing dynamic interface structure as visualizable data, and outlines three general approaches to integration. Metavisualization has been implemented in improvise, using views, lenses, and embedding to reveal the dynamic structure of its own highly coordinated visualizations. Weaver, C. InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) coordination) exploratory visualization linked views metavisualization software visualization 2005 infovis05--1532144 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Two-tone pseudo coloring: compact visualization for one-dimensional data. A new pseudo coloring technique for large scale one-dimensional datasets is proposed. For visualization of a large scale dataset, user interaction is indispensable for selecting focus areas in the dataset. However, excessive switching of the visualized image makes it difficult for the user to recognize overview/ detail and detail/ detail relationships. The goal of this research is to develop techniques for visualizing details as precisely as possible in overview display. In this paper, visualization of a one-dimensional but very large dataset is considered. The proposed method is based on pseudo coloring, however, each scalar value corresponds to two discrete colors. By painting with two colors at each value, users can read out the value precisely. This method has many advantages: it requires little image space for visualization; both the overview and details of the dataset are visible in one image without distortion; and implementation is very simple. Several application examples, such as meteorological observation data and train convenience evaluation data, show the effectiveness of the method. Hoshiya, Y. Kaseda, T. Miyamura, H.N. Saito, H. Saito, T. Yamamoto, M. distortion evaluation interaction overview InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) data density) detail focus+context overview pseudo color 2005 infovis05--1532145 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) A note on space-filling visualizations and space-filling curves. A recent line of treemap research has focused on layout algorithms that optimize properties such as stability, preservation of ordering information, and aspect ratio of rectangles. No ideal treemap layout algorithm has been found, and so it is natural to explore layouts that produce nonrectangular regions. This note describes a connection between space-filling visualizations and the mathematics of space-filling curves, and uses that connection to characterize a family of layout algorithms which produce nonrectangular regions but enjoy geometric continuity under changes to the data and legibility even for highly unbalanced trees. Wattenberg, M. treemap InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) 2005) infovis05--1532146 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) An optimization-based approach to dynamic visual context management. We are building an intelligent multimodal conversation system to aid users in exploring large and complex data sets. To tailor to diverse user queries introduced during a conversation, we automate the generation of system responses, including both spoken and visual outputs. In this paper, we focus on the problem of visual context management, a process that dynamically updates an existing visual display to effectively incorporate new information requested by subsequent user queries. Specifically, we develop an optimization based approach to visual context management. Compared to existing approaches, which normally handle predictable visual context updates, our work offers two unique contributions. First, we provide a general computational framework that can effectively manage a visual context for diverse, unanticipated situations encountered in a user system conversation. Moreover, we optimize the satisfaction of both semantic and visual constraints, which otherwise are difficult to balance using simple heuristics. Second, we present an extensible representation model that uses feature based metrics to uniformly define all constraints. We have applied our work to two different applications and our evaluation has shown the promise of this work. Aggarwal, V. Wen, Z. Zhou, M.X. evaluation metrics InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) automated generation of visualization) intelligent multimodal interface visual context management visual momentum 2005 infovis05--1532147 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Adapting the cognitive walkthrough method to assess the usability of a knowledge domain visualization. The usability of knowledge domain visualization (KDViz) tools can be assessed at several levels. Cognitive walkthrough (CW) is a well known usability inspection method that focuses on how easily users can learn software through exploration. Typical applications of CW follow structured tasks where user goals and action sequences that lead to achievement of the goals are well defined. KDViz and other information visualization tools, however, are typically designed for users to explore data and user goals and actions are less well understood. In this paper, we describe how the traditional CW method may be adapted for assessing the usability of these systems. We apply the adapted version of CW to CiteSpace, a KDViz tool that uses bibliometric analyses to create visualizations of scientific literatures. We describe usability issues identified by the adapted CW and discuss how CiteSpace supported the completion of tasks, such as identifying research fronts, and the achievement of goals. Finally, we discuss improvements to the adapted CW and issues to be addressed before applying it to a wider range of KDViz tools. Allendoerfer, K. Aluker, S. Chen, C. Panjwani, G. Proctor, J. Sturtz, D. Vukovic, M. usability InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) bibliographic networks) cognitive walkthrough usability inspection methods 2005 infovis05--1532148 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Importance-driven visualization layouts for large time series data. Time series are an important type of data with applications in virtually every aspect of the real world. Often a large number of time series have to be monitored and analyzed in parallel. Sets of time series may show intrinsic hierarchical relationships and varying degrees of importance among the individual time series. Effective techniques for visually analyzing large sets of time series should encode the relative importance and hierarchical ordering of the time series data by size and position, and should also provide a high degree of regularity in order to support comparability by the analyst. In this paper, we present a framework for visualizing large sets of time series. Based on the notion of inter time series importance relationships, we define a set of objective functions that space-filling layout schemes for time series data should obey. We develop an efficient algorithm addressing the identified problems by generating layouts that reflect hierarchy and importance based relationships in a regular layout with favorable aspect ratios. We apply our technique to a number of real world data sets including sales and stock data, and we compare our technique with an aspect ratio aware variant of the well known TreeMap algorithm. The examples show the advantages and practical usefulness of our layout algorithm. Dayal, U. Hao, M.C. Keim, D.A. Schreck, T. hierarchy time series treemap InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) information visualization) space-filling layout generation time series 2005 infovis05--1532149 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Temporal visualization of planning polygons for efficient partitioning of geo-spatial data. Partitioning of geo-spatial data for efficient allocation of resources such as schools and emergency health care services is driven by a need to provide better and more effective services. Partitioning of spatial data is a complex process that depends on numerous factors such as population, costs incurred in deploying or utilizing resources and target capacity of a resource. Moreover, complex data such as population distributions are dynamic i.e. they may change over time. Simple animation may not effectively show temporal changes in spatial data. We propose the use of three temporal visualization techniques -wedges, rings and time slices - to display the nature of change in temporal data in a single view. Along with maximizing resource utilization and minimizing utilization costs, a partition should also ensure the long term effectiveness of the plan. We use multi-attribute visualization techniques to highlight the strengths and identify the weaknesses of a partition. Comparative visualization techniques allow multiple partitions to be viewed simultaneously. Users can make informed decisions about how to partition geo spatial data by using a combination of our techniques for multi-attribute visualization, temporal visualization and comparative visualization. Rheingans, P. Shanbhag, P. desJardins, M. animation InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) multi-attribute visualization) resource allocation spatial data temporal visualization time-dependent attributes 2005 infovis05--1532150 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Flow map layout. Cartographers have long used flow maps to show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network. The advantage of flow maps is that they reduce visual clutter by merging edges. Most flow maps are drawn by hand and there are few computer algorithms available. We present a method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes. Our techniques are inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another. We demonstrate our technique by producing flow maps for network traffic, census data, and trade data. Hanrahan, P. Phan, D. Xiao, L. Yeh, R. clustering graph graph layout network InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) GIS) flow maps hierarchical clustering 2005 infovis05--1532151 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Visualization of graphs with associated timeseries data. The most common approach to support analysis of graphs with associated time series data include: overlay of data on graph vertices for one timepoint at a time by manipulating a visual property (e.g. color) of the vertex, along with sliders or some such mechanism to animate the graph for other timepoints. Alternatively, data from all the timepoints can be overlaid simultaneously by embedding small charts into graph vertices. These graph visualizations may also be linked to other visualizations (e.g., parallel co-ordinates) using brushing and linking. This paper describes a study performed to evaluate and rank graph+timeseries visualization options based on users' performance time and accuracy of responses on predefined tasks. The results suggest that overlaying data on graph vertices one timepoint at a time may lead to more accurate performance for tasks involving analysis of a graph at a single timepoint, and comparisons between graph vertices for two distinct timepoints. Overlaying data simultaneously for all the timepoints on graph vertices may lead to more accurate and faster performance for tasks involving searching for outlier vertices displaying different behavior than the rest of the graph vertices for all timepoints. Single views have advantage over multiple views on tasks that require topological information. Also, the number of attributes displayed on nodes has a non trivial influence on accuracy of responses, whereas the number of visualizations affect the performance time. Lee, P. North, C. Saraiya, P. brushing color graph multiple views time series InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) data overlay) graph visualization time series data analysis usability experiments 2005 infovis05--1532152 10/23/2005 2005 IEEE Symposium on Information Visualization (InfoVis) Interactive Sankey diagrams. We present a system that allows users to interactively explore complex flow scenarios represented as Sankey diagrams. Our system provides an overview of the flow graph and allows users to zoom in and explore details on demand. The support for quantitative flow tracing across the flow graph as well as representations at different levels of detail facilitate the understanding of complex flow situations. The energy flow in a city serves as a sample scenario for our system. Different forms of energy are distributed within the city and they are transformed into heat, electricity, or other forms of energy. These processes are visualized and interactively explored. In addition our system can be used as a planning tool for the exploration of alternative scenarios by interactively manipulating different parameters in the energy flow network. Froehlich, B. Hanfler, M. Riehmann, P. graph network overview zoom InfoVis 2005 IEEE Symposium on Information Visualization (InfoVis) Sankey diagram) flow diagram 2005 infovis06--4015416 10/29/2006 IEEE Transactions on Visualization and Computer Graphics ASK-GraphView: A Large Scale Graph Visualization System. We describe ASK-GraphView, a node-link-based graph visualization system that allows clustering and interactive navigation of large graphs, ranging in size up to 16 million edges. The system uses a scalable architecture and a series of increasingly sophisticated clustering algorithms to construct a hierarchy on an arbitrary, weighted undirected input graph. By lowering the interactivity requirements we can scale to substantially bigger graphs. The user is allowed to navigate this hierarchy in a top down manner by interactively expanding individual clusters. ASK-GraphView also provides facilities for filtering and coloring, annotation and cluster labeling. Abello, J. Krishnan, N. van Ham, F. cluster clustering graph hierarchy navigation InfoVis IEEE Transactions on Visualization and Computer Graphics graph clustering graph visualization information visualization 2006 infovis06--4015417 10/29/2006 IEEE Transactions on Visualization and Computer Graphics MatrixExplorer: a Dual-Representation System to Explore Social Networks. MatrixExplorer is a network visualization system that uses two representations: node-link diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrix-based representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrix-based representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports node-link diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and node-link representations are kept synchronized at all stages of the exploration process. Fekete, J.-D. Henry Riche, N. clustering matrix network social InfoVis IEEE Transactions on Visualization and Computer Graphics consensus exploratory process interactive clustering matrix ordering matrix-based representations node-link diagram social networks visualization 2006 infovis06--4015418 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Multivariate State Transition Graphs. We present a new approach for the visual analysis of state transition graphs. We deal with multivariate graphs where a number of attributes are associated with every node. Our method provides an interactive attribute-based clustering facility. Clustering results in metric, hierarchical and relational data, represented in a single visualization. To visualize hierarchically structured quantitative data, we introduce a novel technique: the bar tree. We combine this with a node-link diagram to visualize the hierarchy and an arc diagram to visualize relational data. Our method enables the user to gain significant insight into large state transition graphs containing tens of thousands of nodes. We illustrate the effectiveness of our approach by applying it to a real-world use case. The graph we consider models the behavior of an industrial wafer stepper and contains 55,043 nodes and 289,443 edges. Pretorius, A.J. van Wijk, J.J. clustering graph hierarchy insight InfoVis IEEE Transactions on Visualization and Computer Graphics finite state machines graph visualization interactive clustering multivariate visualization state spaces transition systems 2006 infovis06--4015419 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Balancing Systematic and Flexible Exploration of Social Networks. Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks. Perer, A. Shneiderman, B. coordinated views filter matrix network overview social InfoVis IEEE Transactions on Visualization and Computer Graphics attribute ranking coordinated views exploratory data analysis interactive graph visualization social networks 2006 infovis06--4015420 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Multi-Scale Banking to 45 Degrees. In his text Visualizing Data, William Cleveland demonstrates how the aspect ratio of a line chart can affect an analyst's perception of trends in the data. Cleveland proposes an optimization technique for computing the aspect ratio such that the average absolute orientation of line segments in the chart is equal to 45 degrees. This technique, called banking to 45deg, is designed to maximize the discriminability of the orientations of the line segments in the chart. In this paper, we revisit this classic result and describe two new extensions. First, we propose alternate optimization criteria designed to further improve the visual perception of line segment orientations. Second, we develop multi-scale banking, a technique that combines spectral analysis with banking to 45deg. Our technique automatically identifies trends at various frequency scales and then generates a banked chart for each of these scales. We demonstrate the utility of our techniques in a range of visualization tools and analysis examples. Agrawala, M. Heer, J. perception text InfoVis IEEE Transactions on Visualization and Computer Graphics banking to 45 degrees graphical perception information visualization line charts sparklines time series 2006 infovis06--4015421 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Measuring Data Abstraction Quality in Multiresolution Visualizations. Data abstraction techniques are widely used in multiresolution visualization systems to reduce visual clutter and facilitate analysis from overview to detail. However, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the abstractions. In this paper, we define two data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the histogram difference measure and the nearest neighbor measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the acceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level. Also, analysts can compare different abstraction methods using the measures to see how well relative data density and outliers are maintained, and then select an abstraction method that meets the requirement of their analytic tasks. Cui, Q. Rundensteiner, E.A. Ward, M.O. Yang, J. clustering overview InfoVis IEEE Transactions on Visualization and Computer Graphics clustering metrics multiresolution visualization sampling 2006 infovis06--4015422 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Enabling Automatic Clutter Reduction in Parallel Coordinate Plots. We have previously shown that random sampling is an effective clutter reduction technique and that a sampling lens can facilitate focus+context viewing of particular regions. This demands an efficient method of estimating the overlap or occlusion of large numbers of intersecting lines in order to automatically adjust the sampling rate within the lens. This paper proposes several ways for measuring occlusion in parallel coordinate plots. An empirical study into the accuracy and efficiency of the occlusion measures show that a probabilistic approach combined with a 'binning' technique is very fast and yet approaches the accuracy of the more expensive 'true' complete measurement. Dix, A. Ellis, G. focus+context occlusion InfoVis IEEE Transactions on Visualization and Computer Graphics clutter density reduction information visualization lens occlusion overplotting parallel coordinates random sampling sampling 2006 infovis06--4015423 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Topographic Visualization of Prefix Propagation in the Internet. We propose a new metaphor for the visualization of prefixes propagation in the Internet. Such a metaphor is based on the concept of topographic map and allows to put in evidence the relative importance of the Internet Service Providers (ISPs) involved in the routing of the prefix. Based on the new metaphor we propose an algorithm for computing layouts and experiment with such algorithm on a test suite taken from the real Internet. The paper extends the visualization approach of the BGPlay service, which is an Internet routing monitoring tool widely used by ISP operators. Cortese, P.F. Di Battista, G. Moneta, A. Patrignani, M. Pizzonia, M. experiment InfoVis IEEE Transactions on Visualization and Computer Graphics graph drawing interdomain routing internet visualization spring embedder 2006 infovis06--4015424 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Network Visualization by Semantic Substrates. Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations. Aris, A. Shneiderman, B. network InfoVis IEEE Transactions on Visualization and Computer Graphics graphical user interface information visualization network visualization semantic substrate 2006 infovis06--4015425 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations. Holten, D. evaluation graph hierarchy InfoVis IEEE Transactions on Visualization and Computer Graphics curves edge aggregation edge bundling edge concentration graph visualization hierarchies network visualization node-link diagram tree visualization treemap 2006 infovis06--4015426 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Visualization of Geo-spatial Point Sets via Global Shape Transformation and Local Pixel Placement. In many applications, data is collected and indexed by geo-spatial location. Discovering interesting patterns through visualization is an important way of gaining insight about such data. A previously proposed approach is to apply local placement functions such as PixelMaps that transform the input data set into a solution set that preserves certain constraints while making interesting patterns more obvious and avoid data loss from overplotting. In experience, this family of spatial transformations can reveal fine structures in large point sets, but it is sometimes difficult to relate those structures to basic geographic features such as cities and regional boundaries. Recent information visualization research has addressed other types of transformation functions that make spatially-transformed maps with recognizable shapes. These types of spatial-transformation are called global shape functions. In particular, cartogram-based map distortion has been studied. On the other hand, cartogram-based distortion does not handle point sets readily. In this study, we present a framework that allows the user to specify a global shape function and a local placement function. We combine cartogram-based layout (global shape) with PixelMaps (local placement), obtaining some of the benefits of each toward improved exploration of dense geo-spatial data sets. Keim, D.A. North, S.C. Panse, C. Sips, M. distortion geographic insight pixel InfoVis IEEE Transactions on Visualization and Computer Graphics cartogram geo-spatial data pixel placement shape transformation 2006 infovis06--4015427 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Worldmapper: The World as YouA˘Żve Never Seen it Before. This paper describes the Worldmapper project, which makes use of novel visualization techniques to represent a broad variety of social and economic data about the countries of the world. The goal of the project is to use the map projections known as cartograms to depict comparisons and relations between different territories, and its execution raises many interesting design challenges that were not all apparent at the outset. We discuss the approaches taken towards these challenges, some of which may have considerably broad application. We conclude by commenting on the positive initial response to the Worldmapper images published on the Web, which we believe is due, at least in part, to the particular effectiveness of the cartogram as a tool for communicating quantitative geographic data. Barford, A. Dorling, D. Newman, M. geographic social InfoVis IEEE Transactions on Visualization and Computer Graphics Worldmapper cartogram computer graphics data visualization geographic visualization social visualization 2006 infovis06--4015428 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Spatial Analysis of News Sources. People in different places talk about different things. This interest distribution is reflected by the newspaper articles circulated in a particular area. We use data from our large-scale newspaper analysis system (Lydia) to make entity datamaps, a spatial visualization of the interest in a given named entity. Our goal is to identify entities which display regional biases. We develop a model of estimating the frequency of reference of an entity in any given city from the reference frequency centered in surrounding cities, and techniques for evaluating the spatial significance of this distribution. Bao, Y. Li, X. Mehler, A. Skiena, S. Wang, Y. InfoVis IEEE Transactions on Visualization and Computer Graphics GIS WWW data visualization geographic visualization information analytics newspapers spidering text and document visualization 2006 infovis06--4015429 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Dynamic Map Labeling. We address the problem of filtering, selecting and placing labels on a dynamic map, which is characterized by continuous zooming and panning capabilities. This consists of two interrelated issues. The first is to avoid label popping and other artifacts that cause confusion and interrupt navigation, and the second is to label at interactive speed. In most formulations the static map labeling problem is NP-hard, and a fast approximation might have O(n log n) complexity. Even this is too slow during interaction, when the number of labels shown can be several orders of magnitude less than the number in the map. In this paper we introduce a set of desiderata for "consistent" dynamic map labeling, which has qualities desirable for navigation. We develop a new framework for dynamic labeling that achieves the desiderata and allows for fast interactive display by moving all of the selection and placement decisions into the preprocessing phase. This framework is general enough to accommodate a variety of selection and placement algorithms. It does not appear possible to achieve our desiderata using previous frameworks. Prior to this paper, there were no formal models of dynamic maps or of dynamic labels; our paper introduces both. We formulate a general optimization problem for dynamic map labeling and give a solution to a simple version of the problem. The simple version is based on label priorities and a versatile and intuitive class of dynamic label placements we call "invariant point placements". Despite these restrictions, our approach gives a useful and practical solution. Our implementation is incorporated into the G-Vis system which is a full-detail dynamic map of the continental USA. This demo is available through any browser. Been, K. Daiches, E. Yap, C. interaction navigation zooming InfoVis IEEE Transactions on Visualization and Computer Graphics GIS HCI computational cartography dynamic maps human-computer interface label consistency label filtering label placement label selection map labeling preprocessing realtime 2006 infovis06--4015430 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Visualization of Barrier Tree Sequences. Dynamical models that explain the formation of spatial structures of RNA molecules have reached a complexity that requires novel visualization methods that help to analyze the validity of these models. We focus on the visualization of so-called folding landscapes of a growing RNA molecule. Folding landscapes describe the energy of a molecule as a function of its spatial configuration; thus they are huge and high dimensional. Their most salient features, however, are encapsulated by their so-called barrier tree that reflects the local minima and their connecting saddle points. For each length of the growing RNA chain there exists a folding landscape. We visualize the sequence of folding landscapes by an animation of the corresponding barrier trees. To generate the animation, we adapt the foresight layout with tolerance algorithm for general dynamic graph layout problems. Since it is very general, we give a detailed description of each phase: constructing a supergraph for the trees, layout of that supergraph using a modified DOT algorithm, and presentation techniques for the final animation. Flamm, C. Heine, C. Hofacker, I.L. Scheuermann, G. Stadler, P.F. animation graph graph layout InfoVis IEEE Transactions on Visualization and Computer Graphics RNA folding barrier tree dynamic graph energy landscape fitness landscape graph drawing 2006 infovis06--4015431 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Visualizing Business Data with Generalized Treemaps. Business data is often presented using simple business graphics. These familiar visualizations are effective for providing overviews, but fall short for the presentation of large amounts of detailed information. Treemaps can provide such detail, but are often not easy to understand. We present how standard treemap algorithms can be adapted such that the results mimic familiar business graphics. Specifically, we present the use of different layout algorithms per level, a number of variations of the squarified algorithm, the use of variable borders, and the use of non-rectangular shapes. The combined use of these leads to histograms, pie charts and a variety of other styles. Vliegen, R. van Wijk, J.J. van der Linden, E.-J. business treemap InfoVis IEEE Transactions on Visualization and Computer Graphics business graphics hierarchical data information visualization treemap 2006 infovis06--4015432 10/29/2006 IEEE Transactions on Visualization and Computer Graphics FacetMap: A Scalable Search and Browse Visualization. The dominant paradigm for searching and browsing large data stores is text-based: presenting a scrollable list of search results in response to textual search term input. While this works well for the Web, there is opportunity for improvement in the domain of personal information stores, which tend to have more heterogeneous data and richer metadata. In this paper, we introduce FacetMap, an interactive, query-driven visualization, generalizable to a wide range of metadata-rich data stores. FacetMap uses a visual metaphor for both input (selection of metadata facets as filters) and output. Results of a user study provide insight into tradeoffs between FacetMap's graphical approach and the traditional text-oriented approach. Czerwinski, M. Meyers, B.R. Robertson, G. Smith, G. Tan, D.S. insight text user study InfoVis IEEE Transactions on Visualization and Computer Graphics faceted metadata graphical visualization interactive information retrieval 2006 infovis06--4015433 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Visual Exploration of Complex Time-Varying Graphs. Many graph drawing and visualization algorithms, such as force-directed layout and line-dot rendering, work very well on relatively small and sparse graphs. However, they often produce extremely tangled results and exhibit impractical running times for highly non-planar graphs with large edge density. And very few graph layout algorithms support dynamic time-varying graphs; applying them independently to each frame produces distracting temporally incoherent visualizations. We have developed a new visualization technique based on a novel approach to hierarchically structuring dense graphs via stratification. Using this structure, we formulate a hierarchical force-directed layout algorithm that is both efficient and produces quality graph layouts. The stratification of the graph also allows us to present views of the data that abstract away many small details of its structure. Rather than displaying all edges and nodes at once, resulting in a convoluted rendering, we present an interactive tool that filters edges and nodes using the graph hierarchy and allows users to drill down into the graph for details. Our layout algorithm also accommodates time-varying graphs in a natural way, producing a temporally coherent animation that can be used to analyze and extract trends from dynamic graph data. For example, we demonstrate the use of our method to explore financial correlation data for the U.S. stock market in the period from 1990 to 2005. The user can easily analyze the time-varying correlation graph of the market, uncovering information such as market sector trends, representative stocks for portfolio construction, and the interrelationship of stocks over time. Garland, M. Kumar, G. bioinformatics graph InfoVis IEEE Transactions on Visualization and Computer Graphics financial data visualization graph and network visualization hierarchy visualization time series data 2006 infovis06--4015434 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Smashing Peacocks Further: Drawing Quasi-Trees from Biconnected Components. Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved quality. Archambault, D. Auber, D. Munzner, T. graph high-dimensional data network InfoVis IEEE Transactions on Visualization and Computer Graphics Quasi-Tree graph and network visualization 2006 infovis06--4015435 10/29/2006 IEEE Transactions on Visualization and Computer Graphics IPSep-CoLa: An Incremental Procedure for Separation Constraint Layout of Graphs. We extend the popular force-directed approach to network (or graph) layout to allow separation constraints, which enforce a minimum horizontal or vertical separation between selected pairs of nodes. This simple class of linear constraints is expressive enough to satisfy a wide variety of application-specific layout requirements, including: layout of directed graphs to better show flow; layout with non-overlapping node labels; and layout of graphs with grouped nodes (called clusters). In the stress majorization force-directed layout process, separation constraints can be treated as a quadratic programming problem. We give an incremental algorithm based on gradient projection for efficiently solving this problem. The algorithm is considerably faster than using generic constraint optimization techniques and is comparable in speed to unconstrained stress majorization. We demonstrate the utility of our technique with sample data from a number of practical applications including gene-activation networks, terrorist networks and visualization of high-dimensional data. Dwyer, T. Koren, Y. Marriott, K. database distortion fisheye interaction navigation overview scatterplot usability user study zoom InfoVis IEEE Transactions on Visualization and Computer Graphics constraints force-directed algorithms graph drawing layout multidimensional scaling stress majorization 2006 infovis06--4015436 10/29/2006 IEEE Transactions on Visualization and Computer Graphics User Interaction with Scatterplots on Small Screens - A Comparative Evaluation of Geometric-Semantic Zoom and Fisheye Distortion. Existing information-visualization techniques that target small screens are usually limited to exploring a few hundred items. In this article we present a scatterplot tool for Personal Digital Assistants that allows the handling of many thousands of items. The application's scalability is achieved by incorporating two alternative interaction techniques: a geometric-semantic zoom that provides smooth transition between overview and detail, and a fisheye distortion that displays the focus and context regions of the scatterplot in a single view. A user study with 24 participants was conducted to compare the usability and efficiency of both techniques when searching a book database containing 7500 items. The study was run on a pen-driven Wacom board simulating a PDA interface. While the results showed no significant difference in task-completion times, a clear majority of 20 users preferred the fisheye view over the zoom interaction. In addition, other dependent variables such as user satisfaction and subjective rating of orientation and navigation support revealed a preference for the fisheye distortion. These findings partly contradict related research and indicate that, when using a small screen, users place higher value on the ability to preserve navigational context than they do on the ease of use of a simplistic, metaphor-based interaction style. Buering, T. Gerken, J. Reiterer, H. distortion evaluation experiment fisheye interaction overview small multiples zoom InfoVis IEEE Transactions on Visualization and Computer Graphics PDA fisheye focus+context scatterplot small screen zoom 2006 infovis06--4015437 10/29/2006 IEEE Transactions on Visualization and Computer Graphics The Perceptual Scalability of Visualization. Larger, higher resolution displays can be used to increase the scalability of information visualizations. But just how much can scalability increase using larger displays before hitting human perceptual or cognitive limits? Are the same visualization techniques that are good on a single monitor also the techniques that are best when they are scaled up using large, high-resolution displays? To answer these questions we performed a controlled experiment on user performance time, accuracy, and subjective workload when scaling up data quantity with different space-time-attribute visualizations using a large, tiled display. Twelve college students used small multiples, embedded bar matrices, and embedded time-series graphs either on a 2 megapixel (Mp) display or with data scaled up using a 32 Mp tiled display. Participants performed various overview and detail tasks on geospatially-referenced multidimensional time-series data. Results showed that current designs are perceptually scalable because they result in a decrease in task completion time when normalized per number of data attributes along with no decrease in accuracy. It appears that, for the visualizations selected for this study, the relative comparison between designs is generally consistent between display sizes. However, results also suggest that encoding is more important on a smaller display while spatial grouping is more important on a larger display. Some suggestions for designers are provided based on our experience designing visualizations for large displays. North, C. Yost, B. experiment overview small multiples InfoVis IEEE Transactions on Visualization and Computer Graphics empirical evaluation information visualization large displays 2006 infovis06--4015438 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Complex Logarithmic Views for Small Details in Large Contexts. Commonly known detail in context techniques for the two-dimensional Euclidean space enlarge details and shrink their context using mapping functions that introduce geometrical compression. This makes it difficult or even impossible to recognize shapes for large differences in magnification factors. In this paper we propose to use the complex logarithm and the complex root functions to show very small details even in very large contexts. These mappings are conformal, which means they only locally rotate and scale, thus keeping shapes intact and recognizable. They allow showing details that are orders of magnitude smaller than their surroundings in combination with their context in one seamless visualization. We address the utilization of this universal technique for the interaction with complex two-dimensional data considering the exploration of large graphs and other examples. Balzer, M. Bottger, J. Deussen, O. interaction InfoVis IEEE Transactions on Visualization and Computer Graphics analytic functions complex logarithm conformal mappings detail in context interaction 2006 infovis06--4015439 10/29/2006 IEEE Transactions on Visualization and Computer Graphics Software Design Patterns for Information Visualization. Despite a diversity of software architectures supporting information visualization, it is often difficult to identify, evaluate, and re-apply the design solutions implemented within such frameworks. One popular and effective approach for addressing such difficulties is to capture successful solutions in design patterns, abstract descriptions of interacting software components that can be customized to solve design problems within a particular context. Based upon a review of existing frameworks and our own experiences building visualization software, we present a series of design patterns for the domain of information visualization. We discuss the structure, context of use, and interrelations of patterns spanning data representation, graphics, and interaction. By representing design knowledge in a reusable form, these patterns can be used to facilitate software design, implementation, and evaluation, and improve developer education and communication. Agrawala, M. Heer, J. education evaluation interaction InfoVis IEEE Transactions on Visualization and Computer Graphics design patterns information visualization object-oriented programming software engineering 2006 infovis07--4376129 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Network Traffic for Resource Planning, Interactive Monitoring, and Interpretation of Security Threats. The Internet has become a wild place: malicious code is spread on personal computers across the world, deploying botnets ready to attack the network infrastructure. The vast number of security incidents and other anomalies overwhelms attempts at manual analysis, especially when monitoring service provider backbone links. We present an approach to interactive visualization with a case study indicating that interactive visualization can be applied to gain more insight into these large data sets. We superimpose a hierarchy on IP address space, and study the suitability of Treemap variants for each hierarchy level. Because viewing the whole IP hierarchy at once is not practical for most tasks, we evaluate layout stability when eliding large parts of the hierarchy, while maintaining the visibility and ordering of the data of interest. Keim, D.A. Mansmann, F. North, S.C. Rexroad, B. Sheleheda, D. case study hierarchy insight network security treemap InfoVis IEEE Transactions on Visualization and Computer Graphics information visualization network monitoring network security treemap 2007 infovis07--4376130 10/28/2007 IEEE Transactions on Visualization and Computer Graphics AdaptiviTree: Adaptive Tree Visualization for Tournament-Style Brackets. Online pick'em games, such as the recent NCAA college basketball March Madness tournament, form a large and rapidly growing industry. In these games, players make predictions on a tournament bracket that defines which competitors play each other and how they proceed toward a single champion. Throughout the course of the tournament, players monitor the brackets to track progress and to compare predictions made by multiple players. This is often a complex sense making task. The classic bracket visualization was designed for use on paper and utilizes an incrementally additive system in which the winner of each match-up is rewritten in the next round as the tournament progresses. Unfortunately, this representation requires a significant amount of space and makes it relatively difficult to get a quick overview of the tournament state since competitors take arbitrary paths through the static bracket. In this paper, we present AdaptiviTree, a novel visualization that adaptively deforms the representation of the tree and uses its shape to convey outcome information. AdaptiviTree not only provides a more compact and understandable representation, but also allows overlays that display predictions as well as other statistics. We describe results from a lab study we conducted to explore the efficacy of AdaptiviTree, as well as from a deployment of the system in a recent real-world sports tournament. Lee, B. Robertson, G. Smith, G. Tan, D.S. overview statistics InfoVis IEEE Transactions on Visualization and Computer Graphics adaptive tree visualization bracket online fantasy sports picks tournament 2007 infovis07--4376131 10/28/2007 IEEE Transactions on Visualization and Computer Graphics ManyEyes: a Site for Visualization at Internet Scale. We describe the design and deployment of Many Eyes, a public Web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a large scale by fostering a social style of data analysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users. Kriss, J. McKeon, M. ViĂ©gas, F.B. Wattenberg, M. van Ham, F. collaboration social InfoVis IEEE Transactions on Visualization and Computer Graphics World Wide Web communication-minded visualization social data analysis social software visualization 2007 infovis07--4376132 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Scented Widgets: Improving Navigation Cues with Embedded Visualizations. This paper presents scented widgets, graphical user interface controls enhanced with embedded visualizations that facilitate navigation in information spaces. We describe design guidelines for adding visual cues to common user interface widgets such as radio buttons, sliders, and combo boxes and contribute a general software framework for applying scented widgets within applications with minimal modifications to existing source code. We provide a number of example applications and describe a controlled experiment which finds that users exploring unfamiliar data make up to twice as many unique discoveries using widgets imbued with social navigation data. However, these differences equalize as familiarity with the data increases. Agrawala, M. Heer, J. Willett, W. experiment navigation social InfoVis IEEE Transactions on Visualization and Computer Graphics information foraging information visualization social data analysis social navigation user interface toolkits 2007 infovis07--4376133 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Show Me: Automatic Presentation for Visual Analysis. This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users. Hanrahan, P. Mackinlay, J.D. Stolte, C. InfoVis IEEE Transactions on Visualization and Computer Graphics automatic presentation best practices data visualization graphic design small multiples visual analysis 2007 infovis07--4376134 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Casual Information Visualization: Depictions of Data in Everyday Life. Information visualization has often focused on providing deep insight for expert user populations and on techniques for amplifying cognition through complicated interactive visual models. This paper proposes a new subdomain for infovis research that complements the focus on analytic tasks and expert use. Instead of work-related and analytically driven infovis, we propose casual information visualization (or casual infovis) as a complement to more traditional infovis domains. Traditional infovis systems, techniques, and methods do not easily lend themselves to the broad range of user populations, from expert to novices, or from work tasks to more everyday situations. We propose definitions, perspectives, and research directions for further investigations of this emerging subfield. These perspectives build from ambient information visualization (Skog et al., 2003), social visualization, and also from artistic work that visualizes information (Viegas and Wattenberg, 2007). We seek to provide a perspective on infovis that integrates these research agendas under a coherent vocabulary and framework for design. We enumerate the following contributions. First, we demonstrate how blurry the boundary of infovis is by examining systems that exhibit many of the putative properties of infovis systems, but perhaps would not be considered so. Second, we explore the notion of insight and how, instead of a monolithic definition of insight, there may be multiple types, each with particular characteristics. Third, we discuss design challenges for systems intended for casual audiences. Finally we conclude with challenges for system evaluation in this emerging subfield. Mateas, M. Pousman, Z. Stasko, J. cognition evaluation insight social InfoVis IEEE Transactions on Visualization and Computer Graphics ambient infovis casual information visualization design editorial evaluation social infovis 2007 infovis07--4376135 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Geographically Weighted Visualization: Interactive Graphics for Scale-Varying Exploratory Analysis. We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impement these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's dataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales. Brunsdon, C. Dykes, J. evaluation geographic statistics InfoVis IEEE Transactions on Visualization and Computer Graphics coordinated views directional exploratory data analysis geographical weighting interaction multivariate scale 2007 infovis07--4376136 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Visualizing the History of Living Spaces. The technology available to building designers now makes it possible to monitor buildings on a very large scale. Video cameras and motion sensors are commonplace in practically every office space, and are slowly making their way into living spaces. The application of such technologies, in particular video cameras, while improving security, also violates privacy. On the other hand, motion sensors, while being privacy-conscious, typically do not provide enough information for a human operator to maintain the same degree of awareness about the space that can be achieved by using video cameras. We propose a novel approach in which we use a large number of simple motion sensors and a small set of video cameras to monitor a large office space. In our system we deployed 215 motion sensors and six video cameras to monitor the 3,000-square-meter office space occupied by 80 people for a period of about one year. The main problem in operating such systems is finding a way to present this highly multidimensional data, which includes both spatial and temporal components, to a human operator to allow browsing and searching recorded data in an efficient and intuitive way. In this paper we present our experiences and the solutions that we have developed in the course of our work on the system. We consider this work to be the first step in helping designers and managers of building systems gain access to information about occupants' behavior in the context of an entire building in a way that is only minimally intrusive to the occupants' privacy. Ivanov, Y.A. Kaur, I. Sorokin, A. Wren, C.R. awareness history security InfoVis IEEE Transactions on Visualization and Computer Graphics sensor networks spatio-temporal visualization surveillance timeline user interface 2007 infovis07--4376137 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Legible Cities: Focus-Dependent Multi-Resolution Visualization of Urban Relationships. Numerous systems have been developed to display large collections of data for urban contexts; however, most have focused on layering of single dimensions of data and manual calculations to understand relationships within the urban environment. Furthermore, these systems often limit the user's perspectives on the data, thereby diminishing the user's spatial understanding of the viewing region. In this paper, we introduce a highly interactive urban visualization tool that provides intuitive understanding of the urban data. Our system utilizes an aggregation method that combines buildings and city blocks into legible clusters, thus providing continuous levels of abstraction while preserving the user's mental model of the city. In conjunction with a 3D view of the urban model, a separate but integrated information visualization view displays multiple disparate dimensions of the urban data, allowing the user to understand the urban environment both spatially and cognitively in one glance. For our evaluation, expert users from various backgrounds viewed a real city model with census data and confirmed that our system allowed them to gain more intuitive and deeper understanding of the urban model from different perspectives and levels of abstraction than existing commercial urban visualization systems. Chang, R. Kosara, R. Ribarsky, W. Sauda, E. Wessel, G. evaluation InfoVis IEEE Transactions on Visualization and Computer Graphics information visualization multi-resolution urban models 2007 infovis07--4376138 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup.. Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here. Clarke, K. Dykes, J. Slingsby, A. Wood, J. case study geovisualization interaction InfoVis IEEE Transactions on Visualization and Computer Graphics applications of infovis geographic visualization large dataset visualization multiresolution visualization text and document visualization 2007 infovis07--4376139 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Hotmap: Looking at Geographic Attention. Understanding how people use online maps allows data acquisition teams to concentrate their efforts on the portions of the map that are most seen by users. Online maps represent vast databases, and so it is insufficient to simply look at a list of the most-accessed URLs. Hotmap takes advantage of the design of a mapping system's imagery pyramid to superpose a heatmap of the log files over the original maps. Users' behavior within the system can be observed and interpreted. This paper discusses the imagery acquisition task that motivated Hotmap, and presents several examples of information that Hotmap makes visible. We discuss the design choices behind Hotmap, including logarithmic color schemes; low-saturation background images; and tuning images to explore both infrequently-viewed and frequently-viewed spaces. Fisher, D. color geographic InfoVis IEEE Transactions on Visualization and Computer Graphics GIS geographical visualization heatmap online mapping systems server log analysis social navigation 2007 infovis07--4376140 10/28/2007 IEEE Transactions on Visualization and Computer Graphics VisLink: Revealing Relationships Amongst Visualizations. We present VisLink, a method by which visualizations and the relationships between them can be interactively explored. VisLink readily generalizes to support multiple visualizations, empowers inter-representational queries, and enables the reuse of the spatial variables, thus supporting efficient information encoding and providing for powerful visualization bridging. Our approach uses multiple 2D layouts, drawing each one in its own plane. These planes can then be placed and re-positioned in 3D space: side by side, in parallel, or in chosen placements that provide favoured views. Relationships, connections, and patterns between visualizations can be revealed and explored using a variety of interaction techniques including spreading activation and search filters. Carpendale, S. Collins, C. interaction InfoVis IEEE Transactions on Visualization and Computer Graphics 3D visualization edge aggregation graph visualization hierarchies node-link diagram structural comparison 2007 infovis07--4376141 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Visualization of Heterogeneous Data. Both the resource description framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template. Cammarano, M. Chan, B. Dong, X. Halevy, A. Hanrahan, P. Klingner, J. Talbot, J. graph InfoVis IEEE Transactions on Visualization and Computer Graphics RDF attribute inference data integration 2007 infovis07--4376142 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Sequential Document Visualization. Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization. Dillon, J.V. Lebanon, G. Mao, Y. categorical document time series InfoVis IEEE Transactions on Visualization and Computer Graphics document visualization local fitting multi-resolution analysis 2007 infovis07--4376143 10/28/2007 IEEE Transactions on Visualization and Computer Graphics A Taxonomy of Clutter Reduction for Information Visualisation. Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques. Dix, A. Ellis, G. insight taxonomy InfoVis IEEE Transactions on Visualization and Computer Graphics clutter reduction information visualization large datasets occlusion taxonomy 2007 infovis07--4376144 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Toward a Deeper Understanding of the Role of Interaction in Information Visualization. Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction. Jacko, J.A. Kang, Y. Stasko, J. Yi, J.S. filter interaction InfoVis IEEE Transactions on Visualization and Computer Graphics information visualization interaction interaction techniques taxonomy visual analytics 2007 infovis07--4376145 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Interactive Tree Comparison for Co-located Collaborative Information Visualization. In many domains, increased collaboration has lead to more innovation by fostering the sharing of knowledge, skills, and ideas. Shared analysis of information visualizations does not only lead to increased information processing power, but team members can also share, negotiate, and discuss their views and interpretations on a dataset and contribute unique perspectives on a given problem. Designing technologies to support collaboration around information visualizations poses special challenges and relatively few systems have been designed. We focus on supporting small groups collaborating around information visualizations in a co-located setting, using a shared interactive tabletop display. We introduce an analysis of challenges and requirements for the design of co-located collaborative information visualization systems. We then present a new system that facilitates hierarchical data comparison tasks for this type of collaborative work. Our system supports multi-user input, shared and individual views on the hierarchical data visualization, flexible use of representations, and flexible workspace organization to facilitate group work around visualizations. Carpendale, S. Isenberg, P. collaboration InfoVis IEEE Transactions on Visualization and Computer Graphics co-located work collaboration hierarchical data comparison information visualization 2007 infovis07--4376146 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Animated Transitions in Statistical Data Graphics. In this paper we investigate the effectiveness of animated transitions between common statistical data graphics such as bar charts, pie charts, and scatter plots. We extend theoretical models of data graphics to include such transitions, introducing a taxonomy of transition types. We then propose design principles for creating effective transitions and illustrate the application of these principles in DynaVis, a visualization system featuring animated data graphics. Two controlled experiments were conducted to assess the efficacy of various transition types, finding that animated transitions can significantly improve graphical perception. Heer, J. Robertson, G. perception taxonomy InfoVis IEEE Transactions on Visualization and Computer Graphics animation design experiment information visualization statistical data graphics transitions 2007 infovis07--4376147 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques. Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets and to get access to details, especially to those related to the leaves of the trees. We propose the notion of zoomable treemaps (ZTMs), an hybridization between treemaps and zoomable user interfaces that facilitates the navigation in large hierarchical data sets. By providing a consistent set of interaction techniques, ZTMs make it possible for users to browse through very large data sets (e.g., 700,000 nodes dispatched amongst 13 levels). These techniques use the structure of the displayed data to guide the interaction and provide a way to improve interactive navigation in treemaps. Blanch, R. Lecolinet, E. interaction navigation InfoVis IEEE Transactions on Visualization and Computer Graphics information visualization multi-scale interaction structure-aware navigation zoomable treemaps 2007 infovis07--4376148 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Visualizing Causal Semantics Using Animations. Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8% more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9% faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations. Irani, P.P. Kadaba, N.R. Leboe, J. perception theory InfoVis IEEE Transactions on Visualization and Computer Graphics animated graphs causality graph semantics perception semantics visualization visualizing cause and effect 2007 infovis07--4376149 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Spatialization Design: Comparing Points and Landscapes. Spatializations represent non-spatial data using a spatial layout similar to a map. We present an experiment comparing different visual representations of spatialized data, to determine which representations are best for a non-trivial search and point estimation task. Primarily, we compare point-based displays to 2D and 3D information landscapes. We also compare a colour (hue) scale to a grey (lightness) scale. For the task we studied, point-based spatializations were far superior to landscapes, and 2D landscapes were superior to 3D landscapes. Little or no benefit was found for redundantly encoding data using colour or greyscale combined with landscape height. 3D landscapes with no colour scale (height-only) were particularly slow and inaccurate. A colour scale was found to be better than a greyscale for all display types, but a greyscale was helpful compared to height-only. These results suggest that point-based spatializations should be chosen over landscape representations, at least for tasks involving only point data itself rather than derived information about the data space. Wu, F. Munzner, T. So, W.Y. Sprague, D.W. Tory, M. experiment InfoVis IEEE Transactions on Visualization and Computer Graphics 2D 3D colour greyscale information landscape numerosity points spatialization surface user study 2007 infovis07--4376150 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Weaving Versus Blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. In many applications, it is important to understand the individual values of, and relationships between, multiple related scalar variables defined across a common domain. Several approaches have been proposed for representing data in these situations. In this paper we focus on strategies for the visualization of multivariate data that rely on color mixing. In particular, through a series of controlled observer experiments, we seek to establish a fundamental understanding of the information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving. We begin with a baseline experiment in which we assess participants' abilities to accurately read numerical data encoded in six different basic color scales defined in the L*a*b* color space. We then assess participants' abilities to read combinations of 2, 3, 4 and 6 different data values represented in a common region of the domain, encoded using either color blending or color weaving. In color blending a single mixed color is formed via linear combination of the individual values in L*a*b* space, and in color weaving the original individual colors are displayed side-by-side in a high frequency texture that fills the region. A third experiment was conducted to clarify some of the trends regarding the color contrast and its effect on the magnitude of the error that was observed in the second experiment. The results indicate that when the component colors are represented side-by-side in a high frequency texture, most participants' abilities to infer the values of individual components are significantly improved, relative to when the colors are blended. Participants' performance was significantly better with color weaving particularly when more than 2 colors were used, and even when the individual colors subtended only 3 minutes of visual angle in the texture. However, the information-carrying capacity of the color weaving approach has its limits. - - We found that participants' abilities to accurately interpret each of the individual components in a high frequency color texture typically falls off as the number of components increases from 4 to 6. We found no significant advantages, in either color blending or color weaving, to using color scales based on component hues thatare more widely separated in the L*a*b* color space. Furthermore, we found some indications that extra difficulties may arise when opponent hues are employed. Hagh-Shenas, H. Healey, C. Interrante, V. Kim, S. color experiment InfoVis IEEE Transactions on Visualization and Computer Graphics color color blending color weaving perception visualization 2007 infovis07--4376151 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Overview Use in Multiple Visual Information Resolution Interfaces. In interfaces that provide multiple visual information resolutions (VIR), low-VIR overviews typically sacrifice visual details for display capacity, with the assumption that users can select regions of interest to examine at higher VI Rs. Designers can create low VIRs based on multi-level structure inherent in the data, but have little guidance with single-level data. To better guide design tradeoff between display capacity and visual target perceivability, we looked at overview use in two multiple-VIR interfaces with high-VIR displays either embedded within, or separate from, the overviews. We studied two visual requirements for effective overview and found that participants would reliably use the low-VIR overviews only when the visual targets were simple and had small visual spans. Otherwise, at least 20% chose to use the high-VIR view exclusively. Surprisingly, neither of the multiple-VIR interfaces provided performance benefits when compared to using the high-VIR view alone. However, we did observe benefits in providing side-by-side comparisons for target matching. We conjecture that the high cognitive load of multiple-VIR interface interactions, whether real or perceived, is a more considerable barrier to their effective use than was previously considered. Kincaid, R. Lam, H. Munzner, T. overview InfoVis IEEE Transactions on Visualization and Computer Graphics multiple resolutions overview use user study 2007 infovis07--4376152 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Visualizing Changes of Hierarchical Data using Treemaps. While the treemap is a popular method for visualizing hierarchical data, it is often difficult for users to track layout and attribute changes when the data evolve over time. When viewing the treemaps side by side or back and forth, there exist several problems that can prevent viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to represent layouts, and a lack of direct contrast to highlight differences. In this paper, we present strategies to visualize changes of hierarchical data using treemaps. A new treemap layout algorithm is presented to reduce abrupt layout changes and produce consistent visual patterns. Techniques are proposed to effectively visualize the difference and contrast between two treemap snapshots in terms of the map items' colors, sizes, and positions. Experimental data show that our algorithm can achieve a good balance in maintaining a treemap's stability, continuity, readability, and average aspect ratio. A software tool is created to compare treemaps and generate the visualizations. User studies show that the users can better understand the changes in the hierarchy and layout, and more quickly notice the color and size differences using our method. Shen, H.-W. Tu, Y. color hierarchy treemap InfoVis IEEE Transactions on Visualization and Computer Graphics tree comparison treemap treemap layout algorithm visualize changes 2007 infovis07--4376153 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Exploring Multiple Trees through DAG Representations. We present a directed acyclic graph visualisation designed to allow interaction with a set of multiple classification trees, specifically to find overlaps and differences between groups of trees and individual trees. The work is motivated by the need to find a representation for multiple trees that has the space-saving property of a general graph representation and the intuitive parent-child direction cues present in individual representation of trees. Using example taxonomic data sets, we describe augmentations to the common barycenter DAG layout method that reveal shared sets of child nodes between common parents in a clearer manner. Other interactions such as displaying the multiple ancestor paths of a node when it occurs in several trees, and revealing intersecting sibling sets within the context of a single DAG representation are also discussed. Graham, M. Kennedy, J. graph interaction InfoVis IEEE Transactions on Visualization and Computer Graphics directed acyclic graph multiple trees 2007 infovis07--4376154 10/28/2007 IEEE Transactions on Visualization and Computer Graphics NodeTrix: a Hybrid Visualization of Social Networks. The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results. Fekete, J.-D. Henry Riche, N. McGuffin, M.J. case study hardware interaction matrix network social InfoVis IEEE Transactions on Visualization and Computer Graphics aggregation hybrid visualization interaction matrix visualization network visualization 2007 infovis07--4376155 10/28/2007 IEEE Transactions on Visualization and Computer Graphics Multi-Level Graph Layout on the GPU. This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a well-partitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multi-level scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multi-core architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet service provider) networks is presented. Frishman, Y. Tal, A. graph graph layout InfoVis IEEE Transactions on Visualization and Computer Graphics GPU graph layout graph partitioning 2007 infovis08--4658123 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation. Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets. Dragicevic, P. Elmqvist, N. Fekete, J.-D. interaction matrix navigation overview scatterplot InfoVis IEEE Transactions on Visualization and Computer Graphics interaction multivariate data navigation visual analytics visual exploration visual queries 2008 infovis08--4658124 10/19/2008 IEEE Transactions on Visualization and Computer Graphics A Framework of Interaction Costs in Information Visualization. Interaction cost is an important but poorly understood factor in visualization design. We propose a framework of interaction costs inspired by Normanpsilas Seven Stages of Action to facilitate study. From 484 papers, we collected 61 interaction-related usability problems reported in 32 user studies and placed them into our framework of seven costs: (1) Decision costs to form goals; (2) system-power costs to form system operations; (3) Multiple input mode costs to form physical sequences; (4) Physical-motion costs to execute sequences; (5) Visual-cluttering costs to perceive state; (6) View-change costs to interpret perception; (7) State-change costs to evaluate interpretation. We also suggested ways to narrow the gulfs of execution (2-4) and evaluation (5-7) based on collected reports. Our framework suggests a need to consider decision costs (1) as the gulf of goal formation. Lam, H. evaluation interaction perception usability InfoVis IEEE Transactions on Visualization and Computer Graphics framework information visualization interaction interface evaluation 2008 infovis08--4658125 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Balloon Focus: a Seamless Multi-Focus+Context Method for Treemaps. The treemap is one of the most popular methods for visualizing hierarchical data. When a treemap contains a large number of items, inspecting or comparing a few selected items in a greater level of detail becomes very challenging. In this paper, we present a seamless multi-focus and context technique, called Balloon Focus, that allows the user to smoothly enlarge multiple treemap items served as the foci, while maintaining a stable treemap layout as the context. Our method has several desirable features. First, this method is quite general and can be used with different treemap layout algorithms. Second, as the foci are enlarged, the relative positions among all items are preserved. Third, the foci are placed in a way that the remaining space is evenly distributed back to the non-focus treemap items. When Balloon Focus enlarges the focus items to a maximum degree, the above features ensure that the treemap will maintain a consistent appearance and avoid any abrupt layout changes. In our algorithm, a DAG (Directed Acyclic Graph) is used to maintain the positional constraints, and an elastic model is employed to govern the placement of the treemap items. We demonstrate a treemap visualization system that integrates data query, manual focus selection, and our novel multi-focus+context technique, Balloon Focus, together. A user study was conducted. Results show that with Balloon Focus, users can better perform the tasks of comparing the values and the distribution of the foci. Shen, H.-W. Tu, Y. focus+context graph treemap user study InfoVis IEEE Transactions on Visualization and Computer Graphics fisheye focus+context magnification multi-focus multi-scale viewing treemap visualizing query results 2008 infovis08--4658126 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Multi-Focused Geospatial Analysis Using Probes. Traditional geospatial information visualizations often present views that restrict the user to a single perspective. When zoomed out, local trends and anomalies become suppressed and lost; when zoomed in for local inspection, spatial awareness and comparison between regions become limited. In our model, coordinated visualizations are integrated within individual probe interfaces, which depict the local data in user-defined regions-of-interest. Our probe concept can be incorporated into a variety of geospatial visualizations to empower users with the ability to observe, coordinate, and compare data across multiple local regions. It is especially useful when dealing with complex simulations or analyses where behavior in various localities differs from other localities and from the system as a whole. We illustrate the effectiveness of our technique over traditional interfaces by incorporating it within three existing geospatial visualization systems: an agent-based social simulation, a census data exploration tool, and an 3D GIS environment for analyzing urban change over time. In each case, the probe-based interaction enhances spatial awareness, improves inspection and comparison capabilities, expands the range of scopes, and facilitates collaboration among multiple users. Butkiewicz, T. Chang, R. Dou, W. Ribarsky, W. Wartell, Z. awareness collaboration geospatial interaction social InfoVis IEEE Transactions on Visualization and Computer Graphics focus+context geospatial analysis geospatial visualization multiple-view techniques probes 2008 infovis08--4658127 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Distributed Cognition as a Theoretical Framework for Information Visualization. Even though information visualization (InfoVis) research has matured in recent years, it is generally acknowledged that the field still lacks supporting, encompassing theories. In this paper, we argue that the distributed cognition framework can be used to substantiate the theoretical foundation of InfoVis. We highlight fundamental assumptions and theoretical constructs of the distributed cognition approach, based on the cognitive science literature and a real life scenario. We then discuss how the distributed cognition framework can have an impact on the research directions and methodologies we take as InfoVis researchers. Our contributions are as follows. First, we highlight the view that cognition is more an emergent property of interaction than a property of the human mind. Second, we argue that a reductionist approach to study the abstract properties of isolated human minds may not be useful in informing InfoVis design. Finally we propose to make cognition an explicit research agenda, and discuss the implications on how we perform evaluation and theory building. Liu, Z. Nersessian, N. Stasko, J. cognition evaluation interaction theory InfoVis IEEE Transactions on Visualization and Computer Graphics distributed cognition information visualization interaction representation theory and methods 2008 infovis08--4658128 10/19/2008 IEEE Transactions on Visualization and Computer Graphics EMDialog: Bringing Information Visualization into the Museum. Digital information displays are becoming more common in public spaces such as museums, galleries, and libraries. However, the public nature of these locations requires special considerations concerning the design of information visualization in terms of visual representations and interaction techniques. We discuss the potential for, and challenges of, information visualization in the museum context based on our practical experience with EMDialog, an interactive information presentation that was part of the Emily Carr exhibition at the Glenbow Museum in Calgary. EMDialog visualizes the diverse and multi-faceted discourse about this Canadian artist with the goal to both inform and provoke discussion. It provides a visual exploration environment that offers interplay between two integrated visualizations, one for information access along temporal, and the other along contextual dimensions. We describe the results of an observational study we conducted at the museum that revealed the different ways visitors approached and interacted with EMDialog, as well as how they perceived this form of information presentation in the museum context. Our results include the need to present information in a manner sufficiently attractive to draw attention and the importance of rewarding passive observation as well as both short- and longer term information exploration. Carpendale, S. Hinrichs, U. Schmidt, H. interaction InfoVis IEEE Transactions on Visualization and Computer Graphics artistic information visualization interactive information visualization public displays walk-up-and-use interaction 2008 infovis08--4658129 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation. Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing userspsila history logs and how they have been applied to study usage patterns in Tableau. Agrawala, M. Heer, J. Mackinlay, J.D. Stolte, C. database design study evaluation history interaction InfoVis IEEE Transactions on Visualization and Computer Graphics analysis evaluation history presentation undo visualization 2008 infovis08--4658130 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Who Votes For What? A Visual Query Language for Opinion Data. Surveys and opinion polls are extremely popular in the media, especially in the months preceding a general election. However, the available tools for analyzing poll results often require specialized training. Hence, data analysis remains out of reach for many casual computer users. Moreover, the visualizations used to communicate the results of surveys are typically limited to traditional statistical graphics like bar graphs and pie charts, both of which are fundamentally noninteractive. We present a simple interactive visualization that allows users to construct queries on large tabular data sets, and view the results in real time. The results of two separate user studies suggest that our interface lowers the learning curve for naive users, while still providing enough analytical power to discover interesting correlations in the data. Draper, G. Riesenfeld, R. InfoVis IEEE Transactions on Visualization and Computer Graphics data analysis human-computer interaction radial visualization visual query languages 2008 infovis08--4658131 10/19/2008 IEEE Transactions on Visualization and Computer Graphics VisGets: Coordinated Visualizations for Web-based Information Exploration and Discovery. In common Web-based search interfaces, it can be difficult to formulate queries that simultaneously combine temporal, spatial, and topical data filters. We investigate how coordinated visualizations can enhance search and exploration of information on the World Wide Web by easing the formulation of these types of queries. Drawing from visual information seeking and exploratory search, we introduce VisGets - interactive query visualizations of Web-based information that operate with online information within a Web browser. VisGets provide the information seeker with visual overviews of Web resources and offer a way to visually filter the data. Our goal is to facilitate the construction of dynamic search queries that combine filters from more than one data dimension. We present a prototype information exploration system featuring three linked VisGets (temporal, spatial, and topical), and used it to visually explore news items from online RSS feeds. Carpendale, S. Collins, C. Dork, M. Williamson, C. filter world wide web InfoVis IEEE Transactions on Visualization and Computer Graphics World Wide Web exploratory search information retrieval information visualization visual information seeking 2008 infovis08--4658132 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Vispedia: Interactive Visual Exploration of Wikipedia Data via Search-Based Integration. Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, non-uniform schema that require costly data integration into structured tables before visualization can begin. We present Vispedia, a Web-based visualization system that reduces the cost of this data integration. Users can browse Wikipedia, select an interesting data table, then use a search interface to discover, integrate, and visualize additional columns of data drawn from multiple Wikipedia articles. This interaction is supported by a fast path search algorithm over DBpedia, a semantic graph extracted from Wikipedia's hyperlink structure. Vispedia can also export the augmented data tables produced for use in traditional visualization systems. We believe that these techniques begin to address the "long tail" of visualization by allowing a wider audience to visualize a broader class of data. We evaluated this system in a first-use formative lab study. Study participants were able to quickly create effective visualizations for a diverse set of domains, performing data integration as needed. Cammarano, M. Chan, B. Hanrahan, P. Talbot, J. Wu, L. graph interaction InfoVis IEEE Transactions on Visualization and Computer Graphics Wikipedia data integration information visualization search interface semantic web 2008 infovis08--4658133 10/19/2008 IEEE Transactions on Visualization and Computer Graphics The Word Tree, an Interactive Visual Concordance. We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization. ViĂ©gas, F.B. Wattenberg, M. text InfoVis IEEE Transactions on Visualization and Computer Graphics Many Eyes case study concordance document visualization information retrieval search text visualization 2008 infovis08--4658134 10/19/2008 IEEE Transactions on Visualization and Computer Graphics HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections. Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets. Minghim, R. Paulovich, F.V. document InfoVis IEEE Transactions on Visualization and Computer Graphics hierarchical multidimensional visualization high-dimensional data text and document visualization visual knowledge discovery 2008 infovis08--4658135 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Particle-based labeling: Fast point-feature labeling without obscuring other visual features. In many information visualization techniques, labels are an essential part to communicate the visualized data. To preserve the expressiveness of the visual representation, a placed label should neither occlude other labels nor visual representatives (e.g., icons, lines) that communicate crucial information. Optimal, non-overlapping labeling is an NP-hard problem. Thus, only a few approaches achieve a fast non-overlapping labeling in highly interactive scenarios like information visualization. These approaches generally target the point-feature label placement (PFLP) problem, solving only label-label conflicts. This paper presents a new, fast, solid and flexible 2D labeling approach for the PFLP problem that additionally respects other visual elements and the visual extent of labeled features. The results (number of placed labels, processing time) of our particle-based method compare favorably to those of existing techniques. Although the esthetic quality of non-real-time approaches may not be achieved with our method, it complies with practical demands and thus supports the interactive exploration of information spaces. In contrast to the known adjacent techniques, the flexibility of our technique enables labeling of dense point clouds by the use of non-occluding distant labels. Our approach is independent of the underlying visualization technique, which enables us to demonstrate the application of our labeling method within different information visualization scenarios. Cords, H. Luboschik, M. Schumann, H. InfoVis IEEE Transactions on Visualization and Computer Graphics automatic label placement dynamic labeling information visualization interactive labeling occlusion-free 2008 infovis08--4658136 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Stacked Graphs ? Geometry & Aesthetics. In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility. Byron, L. Wattenberg, M. aesthetics graph InfoVis IEEE Transactions on Visualization and Computer Graphics ThemeRiver aesthetics communication-minded visualization last.fm listening history streamgraph time series 2008 infovis08--4658137 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs serve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model. Barsky, A. Gardy, J. Kincaid, R. Munzner, T. coordinated views graph graph layout insight interaction multiple views parallel coordinates InfoVis IEEE Transactions on Visualization and Computer Graphics design study graph layout small multiples systems biology visualization 2008 infovis08--4658138 10/19/2008 IEEE Transactions on Visualization and Computer Graphics The Shaping of Information by Visual Metaphors. The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization. Kosara, R. Ziemkiewicz, C. evaluation experiment interaction theory treemap InfoVis IEEE Transactions on Visualization and Computer Graphics cognition evaluation hierarchies metaphors visualization theory 2008 infovis08--4658139 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Viz-A-Vis: Toward Visualizing Video through Computer Vision. In the established procedural model of information visualization, the first operation is to transform raw data into data tables. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision. Abowd, G.D. Romero, M. Stasko, J. Summet, J. perception InfoVis IEEE Transactions on Visualization and Computer Graphics image/video analytics sensor analytics spatiotemporal visualization time series data video visualization 2008 infovis08--4658140 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Geometry-Based Edge Clustering for Graph Visualization. Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method. Cui, W. Li, X. Qu, H. Wong, P.C. Zhou, H. clustering color graph InfoVis IEEE Transactions on Visualization and Computer Graphics edge clustering graph visualization mesh visual clutter 2008 infovis08--4658141 10/19/2008 IEEE Transactions on Visualization and Computer Graphics On the Visualization of Social and other Scale-Free Networks. This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the networkpsilas underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach. Garland, M. Hart, J. Hoberock, J. Jia, Y. clustering graph network social InfoVis IEEE Transactions on Visualization and Computer Graphics anisotropic shading betweenness centrality edge filtering scale-free network 2008 infovis08--4658142 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Exploration of Networks using overview+detail with Constraint-based cooperative layout. A standard approach to large network visualization is to provide an overview of the network and a detailed view of a small component of the graph centred around a focal node. The user explores the network by changing the focal node in the detailed view or by changing the level of detail of a node or cluster. For scalability, fast force-based layout algorithms are used for the overview and the detailed view. However, using the same layout algorithm in both views is problematic since layout for the detailed view has different requirements to that in the overview. Here we present a model in which constrained graph layout algorithms are used for layout in the detailed view. This means the detailed view has high-quality layout including sophisticated edge routing and is customisable by the user who can add placement constraints on the layout. Scalability is still ensured since the slower layout techniques are only applied to the small subgraph shown in the detailed view. The main technical innovations are techniques to ensure that the overview and detailed view remain synchronized, and modifying constrained graph layout algorithms to support smooth, stable layout. The key innovation supporting stability are new dynamic graph layout algorithms that preserve the topology or structure of the network when the user changes the focus node or the level of detail by in situ semantic zooming. We have built a prototype tool and demonstrate its use in two application domains, UML class diagrams and biological networks. Dwyer, T. Marriott, K. Schreiber, F. Stuckey, P. Woodward, M. Wybrow, M. cluster graph graph layout network overview zooming InfoVis IEEE Transactions on Visualization and Computer Graphics constraints force-directed algorithms graph drawing multidimensional scaling stress majorization 2008 infovis08--4658143 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Rapid Graph Layout Using Space Filling Curves. Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness. Ma, K.-L. Muelder, C. graph graph layout network InfoVis IEEE Transactions on Visualization and Computer Graphics graph layout information visualization space filling curves 2008 infovis08--4658144 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Evaluating the Use of Data Transformation for Information Visualization. Data transformation, the process of preparing raw data for effective visualization, is one of the key challenges in information visualization. Although researchers have developed many data transformation techniques, there is little empirical study of the general impact of data transformation on visualization. Without such study, it is difficult to systematically decide when and which data transformation techniques are needed. We thus have designed and conducted a two-part empirical study that examines how the use of common data transformation techniques impacts visualization quality, which in turn affects user task performance. Our first experiment studies the impact of data transformation on user performance in single-step, typical visual analytic tasks. The second experiment assesses the impact of data transformation in multi-step analytic tasks. Our results quantify the benefits of data transformation in both experiments. More importantly, our analyses reveal that (1) the benefits of data transformation vary significantly by task and by visualization, and (2) the use of data transformation depends on a user's interaction context. Based on our findings, we present a set of design recommendations that help guide the development and use of data transformation techniques. Wen, Z. Zhou, M.X. experiment interaction InfoVis IEEE Transactions on Visualization and Computer Graphics data cleaning data transformations empirical evaluation user study 2008 infovis08--4658145 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Improving the Readability of Clustered Social Networks using Node Duplication. Exploring communities is an important task in social network analysis. Such communities are currently identified using clustering methods to group actors. This approach often leads to actors belonging to one and only one cluster, whereas in real life a person can belong to several communities. As a solution we propose duplicating actors in social networks and discuss potential impact of such a move. Several visual duplication designs are discussed and a controlled experiment comparing network visualization with and without duplication is performed, using 6 tasks that are important for graph readability and visual interpretation of social networks. We show that in our experiment, duplications significantly improve community-related tasks but sometimes interfere with other graph readability tasks. Finally, we propose a set of guidelines for deciding when to duplicate actors and choosing candidates for duplication, and alternative ways to render them in social network representations. Bezerianos, A. Fekete, J.-D. Henry Riche, N. cluster clustering experiment graph network social InfoVis IEEE Transactions on Visualization and Computer Graphics clustering graph visualization node duplications social networks 2008 infovis08--4658146 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Effectiveness of Animation in Trend Visualization. Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate. Fernandez, R. Fisher, D. Lee, B. Robertson, G. Stasko, J. animation small multiples InfoVis IEEE Transactions on Visualization and Computer Graphics animation design experiment information visualization trends 2008 infovis08--4658147 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Perceptual Organization in User-Generated Graph Layouts. Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were ldquomaskedrdquo by extraneous edges we were able to measure observerspsila sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms. Rogowitz, B. van Ham, F. cluster clustering graph graph layout network InfoVis IEEE Transactions on Visualization and Computer Graphics graph layout network layout visualization perceptual organization user study 2008 infovis08--4658148 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Interactive Visual Analysis of Set-Typed Data. While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item - like one cell in a table - has a list of nGt=0 elements as its value. We present the setpsilaopsilagram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people. Freiler, W. Hauser, H. Matkovic, K. categorical education InfoVis IEEE Transactions on Visualization and Computer Graphics focus+context visualization interactive visual analysis interactive visualization multidimensional multivariate data visualization multiple coordinated views 2008 infovis08--4658149 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Spatially Ordered Treemaps. Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described. Dykes, J. Wood, J. case study color database geographic geovisualization treemap InfoVis IEEE Transactions on Visualization and Computer Graphics CIELab cartogram geographic information geovisualization tree structures treemap 2008 infovis08--4658150 10/19/2008 IEEE Transactions on Visualization and Computer Graphics Visualizing Incomplete and Partially Ranked Data. Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences. Cleveland, W.S. Kidwell, P. Lebanon, G. InfoVis IEEE Transactions on Visualization and Computer Graphics incomplete rankings multidimensional scaling partial rankings 2008 infovis09--5290726 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Mapping Text with Phrase Nets. We present a new technique, the phrase net, for generating visual overviews of unstructured text. A phrase net displays a graph whose nodes are words and whose edges indicate that two words are linked by a user-specified relation. These relations may be defined either at the syntactic or lexical level; different relations often produce very different perspectives on the same text. Taken together, these perspectives often provide an illuminating visual overview of the key concepts and relations in a document or set of documents. ViĂ©gas, F.B. Wattenberg, M. van Ham, F. document graph overview text InfoVis data visualisation phrase nets text analysis text mapping user-specified relation visual overviews IEEE Transactions on Visualization and Computer Graphics natural language processing semantic net tag cloud text visualization 2009 infovis09--5290725 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Exemplar-based Visualization of Large Document Corpus. With the rapid growth of the World Wide Web and electronic information services, text corpus is becoming available online at an incredible rate. By displaying text data in a logical layout (e.g., color graphs), text visualization presents a direct way to observe the documents as well as understand the relationship between them. In this paper, we propose a novel technique, Exemplar-based visualization (EV), to visualize an extremely large text corpus. Capitalizing on recent advances in matrix approximation and decomposition, EV presents a probabilistic multidimensional projection model in the low-rank text subspace with a sound objective function. The probability of each document proportion to the topics is obtained through iterative optimization and embedded to a low dimensional space using parameter embedding. By selecting the representative exemplars, we obtain a compact approximation of the data. This makes the visualization highly efficient and flexible. In addition, the selected exemplars neatly summarize the entire data set and greatly reduce the cognitive overload in the visualization, leading to an easier interpretation of large text corpus. Empirically, we demonstrate the superior performance of EV through extensive experiments performed on the publicly available text data sets. Chen, Y. Dong, M. Hua, J. Wang, L. color document matrix text world wide web InfoVis biology computing data visualisation exemplar-based visualization iterative methods iterative optimization large document corpus matrix approximation optimisation parameter embedding text corpus text visualization IEEE Transactions on Visualization and Computer Graphics exemplar large-scale document visualization multidimensional projection 2009 infovis09--5290724 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Visualizing the Intellectual Structure with Paper-Reference Matrices. Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: information visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization. Chen, C. Li, J. Zhang, J. matrix network InfoVis FP-tree Java Java-based prototype system SDSS Sloan Digital Sky Survey astronomical surveys astronomy computing author analysis citation analysis co-citation relationship co-cited unit data visualisation information source information visualization intellectual structure visualization network theory (graphs) node-link network visualization paper-reference matrix pattern clustering reference analysis reference-reference matrix scientific domain analysis scientific information systems tightly-knit cluster trees (mathematics) visual analysis IEEE Transactions on Visualization and Computer Graphics FP-tree co-citation intellectual structure paper-reference matrix 2009 infovis09--5290723 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Document Cards: A Top Trumps Visualization for Documents. Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images. Deussen, O. Keim, D.A. Oelke, D. Rohrdantz, C. Stoffel, A. Strobelt, H. color document text InfoVis IEEE InfoVis publications advanced text mining compact visualization data mining data visualisation display devices document cards document image processing document structure extraction image color histogram semi-semantic image weighting top trumps document visualization IEEE Transactions on Visualization and Computer Graphics content extraction document collection browsing document visualization visual summary 2009 infovis09--5290722 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Participatory Visualization with Wordle. We discuss the design and usage of ldquoWordle,rdquo a Web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a ldquoparticipatory culturerdquo has arisen around them. Feinberg, J. ViĂ©gas, F.B. Wattenberg, M. color text InfoVis Web sites Web-based tool Wordle Wordle layouts data analysis data visualisation tag-cloud-like displays text analysis text visualisation IEEE Transactions on Visualization and Computer Graphics educational visualization memory participatory culture social data analysis tag cloud text visualization 2009 infovis09--5290721 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing. In serial computation, program profiling is often helpful for optimization of key sections of code. When moving to parallel computation, not only does the code execution need to be considered but also communication between the different processes which can induce delays that are detrimental to performance. As the number of processes increases, so does the impact of the communication delays on performance. For large-scale parallel applications, it is critical to understand how the communication impacts performance in order to make the code more efficient. There are several tools available for visualizing program execution and communications on parallel systems. These tools generally provide either views which statistically summarize the entire program execution or process-centric views. However, process-centric visualizations do not scale well as the number of processes gets very large. In particular, the most common representation of parallel processes is a Gantt chart with a row for each process. As the number of processes increases, these charts can become difficult to work with and can even exceed screen resolution. We propose a new visualization approach that affords more scalability and then demonstrate it on systems running with up to 16,384 processes. Gygi, F. Ma, K.-L. Muelder, C. InfoVis Gantt chart MPI Profiling application program interfaces data visualisation information visualization interprocess communication large-scale parallel computing message passing parallel processes parallel systems program execution program profiling visual analysis IEEE Transactions on Visualization and Computer Graphics MPI Profiling information visualization scalability 2009 infovis09--5290720 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Protovis: A Graphical Toolkit for Visualization. Despite myriad tools for visualizing data, there remains a gap between the notational efficiency of high-level visualization systems and the expressiveness and accessibility of low-level graphical systems. Powerful visualization systems may be inflexible or impose abstractions foreign to visual thinking, while graphical systems such as rendering APIs and vector-based drawing programs are tedious for complex work. We argue that an easy-to-use graphical system tailored for visualization is needed. In response, we contribute Protovis, an extensible toolkit for constructing visualizations by composing simple graphical primitives. In Protovis, designers specify visualizations as a hierarchy of marks with visual properties defined as functions of data. This representation achieves a level of expressiveness comparable to low-level graphics systems, while improving efficiency - the effort required to specify a visualization - and accessibility - the effort required to learn and modify the representation. We substantiate this claim through a diverse collection of examples and comparative analysis with popular visualization tools. Bostock, M. Heer, J. hierarchy toolkit InfoVis Protovis application program interfaces data visualisation data visualization graphical visualization toolkit high-level visualization systems low-level graphical systems rendering (computer graphics) rendering API vector-based drawing programs IEEE Transactions on Visualization and Computer Graphics 2D graphics information visualization toolkit user interface 2009 infovis09--5290719 10/14/2009 IEEE Transactions on Visualization and Computer Graphics A Multi-Threading Architecture to Support Interactive Visual Exploration. During continuous user interaction, it is hard to provide rich visual feedback at interactive rates for datasets containing millions of entries. The contribution of this paper is a generic architecture that ensures responsiveness of the application even when dealing with large data and that is applicable to most types of information visualizations. Our architecture builds on the separation of the main application thread and the visualization thread, which can be cancelled early due to user interaction. In combination with a layer mechanism, our architecture facilitates generating previews incrementally to provide rich visual feedback quickly. To help avoiding common pitfalls of multi-threading, we discuss synchronization and communication in detail. We explicitly denote design choices to control trade-offs. A quantitative evaluation based on the system VI S P L ORE shows fast visual feedback during continuous interaction even for millions of entries. We describe instantiations of our architecture in additional tools. Berger, W. Muigg, P. Piringer, H. Tominski, C. evaluation interaction InfoVis VISPLORE continuous user interaction data visualisation information visualizations interactive visual exploration multi-threading multi-threading architecture software architecture user interfaces visual feedback IEEE Transactions on Visualization and Computer Graphics continuous interaction information visualization architecture layer multi-threading preview 2009 infovis09--5290718 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Towards Utilizing GPUs in Information Visualization: A Model and Implementation of Image-Space Operations. Modern programmable GPUs represent a vast potential in terms of performance and visual flexibility for information visualization research, but surprisingly few applications even begin to utilize this potential. In this paper, we conjecture that this may be due to the mismatch between the high-level abstract data types commonly visualized in our field, and the low-level floating-point model supported by current GPU shader languages. To help remedy this situation, we present a refinement of the traditional information visualization pipeline that is amenable to implementation using GPU shaders. The refinement consists of a final image-space step in the pipeline where the multivariate data of the visualization is sampled in the resolution of the current view. To concretize the theoretical aspects of this work, we also present a visual programming environment for constructing visualization shaders using a simple drag-and-drop interface. Finally, we give some examples of the use of shaders for well-known visualization techniques. Elmqvist, N. McDonnel, B. InfoVis GPU shader languages abstract data types coprocessors data visualisation drag-and-drop interface high-level abstract data type image-space operation information visualization low-level floating-point model visual programming visual programming environment IEEE Transactions on Visualization and Computer Graphics GPU-acceleration high-performance visualization interaction shader programming 2009 infovis09--5290717 10/14/2009 IEEE Transactions on Visualization and Computer Graphics code_swarm: A Design Study in Organic Software Visualization. In May of 2008, we published online a series of software visualization videos using a method called code_swarm. Shortly thereafter, we made the code open source and its popularity took off. This paper is a study of our code swarm application, comprising its design, results and public response. We share our design methodology, including why we chose the organic information visualization technique, how we designed for both developers and a casual audience, and what lessons we learned from our experiment. We validate the results produced by code_swarm through a qualitative analysis and by gathering online user comments. Furthermore, we successfully released the code as open source, and the software community used it to visualize their own projects and shared their results as well. In the end, we believe code_swarm has positive implications for the future of organic information design and open source information visualization practice. Ma, K.-L. Ogawa, M. design study experiment software visualization InfoVis code_swarm data visualisation design methodology open source information visualization practice organic information design organic information visualization technique organic software visualization public domain software software development evolution software development history software maintenance software visualization videos video signal processing IEEE Transactions on Visualization and Computer Graphics organic information visualization software development history and evolution software visualization 2009 infovis09--5290716 10/14/2009 IEEE Transactions on Visualization and Computer Graphics SpicyNodes: Radial Layout Authoring for the General Public. Trees and graphs are relevant to many online tasks such as visualizing social networks, product catalogs, educational portals, digital libraries, the semantic web, concept maps and personalized information management. SpicyNodes is an information-visualization technology that builds upon existing research on radial tree layouts and graph structures. Users can browse a tree, clicking from node to node, as well as successively viewing a node, immediately related nodes and the path back to the ldquohomerdquo nodes. SpicyNodes' layout algorithms maintain balanced layouts using a hybrid mixture of a geometric layout (a succession of spanning radial trees) and force-directed layouts to minimize overlapping nodes, plus several other improvements over prior art. It provides XML-based API and GUI authoring tools. The goal of the SpicyNodes project is to implement familiar principles of radial maps and focus+context with an attractive and inviting look and feel in an open system that is accessible to virtually any Internet user. Ancuta, O. Douma, M. Gritsai, P. Ligierko, G. Liu, S. focus+context graph radial social InfoVis GUI authoring tools SpicyNodes XML-based API authoring systems data visualisation force-directed layouts geometric layout graph structures information-visualization technology radial layout authoring radial tree layouts trees (mathematics) IEEE Transactions on Visualization and Computer Graphics focus+context hierarchy visualization human-computer interaction information visualization interaction radial tree layout trees and network visualization 2009 infovis09--5290715 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Harnessing the Information Ecosystem with Wiki-based Visualization Dashboards. We describe the design and deployment of Dashiki, a public Website where users may collaboratively build visualization dashboards through a combination of a wiki-like syntax and interactive editors. Our goals are to extend existing research on social data analysis into presentation and organization of data from multiple sources, explore new metaphors for these activities, and participate more fully in the Web's information ecology by providing tighter integration with real-time data. To support these goals, our design includes novel and low-barrier mechanisms for editing and layout of dashboard pages and visualizations, connection to data sources, and coordinating interaction between visualizations. In addition to describing these technologies, we provide a preliminary report on the public launch of a prototype based on this design, including a description of the activities of our users derived from observation and interviews. McKeon, M. interaction social InfoVis Dashiki Internet Web information ecology Web information ecosystem Wiki-based visualization dashboards coordinating interaction dashboard pages data analysis data visualisation public Web site social data analysis social networking (online) wiki-like syntax IEEE Transactions on Visualization and Computer Graphics collaboration dashboards social data analysis social software visual analytics visualization web wikis 2009 infovis09--5290714 10/14/2009 IEEE Transactions on Visualization and Computer Graphics The Benefits of Synchronous Collaborative Information Visualization: Evidence from an Experimental Evaluation. A great corpus of studies reports empirical evidence of how information visualization supports comprehension and analysis of data. The benefits of visualization for synchronous group knowledge work, however, have not been addressed extensively. Anecdotal evidence and use cases illustrate the benefits of synchronous collaborative information visualization, but very few empirical studies have rigorously examined the impact of visualization on group knowledge work. We have consequently designed and conducted an experiment in which we have analyzed the impact of visualization on knowledge sharing in situated work groups. Our experimental study consists of evaluating the performance of 131 subjects (all experienced managers) in groups of 5 (for a total of 26 groups), working together on a real-life knowledge sharing task. We compare (1) the control condition (no visualization provided), with two visualization supports: (2) optimal and (3) suboptimal visualization (based on a previous survey). The facilitator of each group was asked to populate the provided interactive visual template with insights from the group, and to organize the contributions according to the group consensus. We have evaluated the results through both objective and subjective measures. Our statistical analysis clearly shows that interactive visualization has a statistically significant, objective and positive impact on the outcomes of knowledge sharing, but that the subjects seem not to be aware of this. In particular, groups supported by visualization achieved higher productivity, higher quality of outcome and greater knowledge gains. No statistically significant results could be found between an optimal and a suboptimal visualization though (as classified by the pre-experiment survey). Subjects also did not seem to be aware of the benefits that the visualizations provided as no difference between the visualization and the control conditions was found for the self-reported measures of satisfaction a- nd participation. An implication of our study for information visualization applications is to extend them by using real-time group annotation functionalities that aid in the group sense making process of the represented data. Bresciani, S. Eppler, M.J. evaluation experiment InfoVis data analysis data visualisation group annotation knowledge sharing statistical analysis suboptimal visualization synchronous collaborative information visualization synchronous group knowledge work IEEE Transactions on Visualization and Computer Graphics collaborative and distributed visualization experiment group work knowledge sharing laboratory study synchronous situated collaboration visual knowledge representation 2009 infovis09--5290713 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Lark: Coordinating Co-located Collaboration with Information Visualization. Large multi-touch displays are expanding the possibilities of multiple-coordinated views by allowing multiple people to interact with data in concert or independently. We present Lark, a system that facilitates the coordination of interactions with information visualizations on shared digital workspaces. We focus on supporting this coordination according to four main criteria: scoped interaction, temporal flexibility, spatial flexibility, and changing collaboration styles. These are achieved by integrating a representation of the information visualization pipeline into the shared workspace, thus explicitly indicating coordination points on data, representation, presentation, and view levels. This integrated meta-visualization supports both the awareness of how views are linked and the freedom to work in concert or independently. Lark incorporates these four main criteria into a coherent visualization collaboration interaction environment by providing direct visual and algorithmic support for the coordination of data analysis actions over shared large displays. Carpendale, S. Isenberg, P. Tobiasz, M. awareness collaboration coordinated views interaction InfoVis Lark colocated collaboration data analysis data visualisation information visualization integrated meta-visualization multitouch displays scoped interaction spatial flexibility temporal flexibility touch sensitive screens IEEE Transactions on Visualization and Computer Graphics co-located work collaboration coordination information visualization meta-visualization workspace awareness 2009 infovis09--5290712 10/14/2009 IEEE Transactions on Visualization and Computer Graphics ResultMaps: Visualization for Search Interfaces. Hierarchical representations are common in digital repositories, yet are not always fully leveraged in their online search interfaces. This work describes ResultMaps, which use hierarchical treemap representations with query string-driven digital library search engines. We describe two lab experiments, which find that ResultsMap users yield significantly better results over a control condition on some subjective measures, and we find evidence that ResultMaps have ancillary benefits via increased understanding of some aspects of repository content. The ResultMap system and experiments contribute an understanding of the benefits-direct and indirect-of the ResultMap approach to repository search visualization. Clarkson, E. Desai, K. Foley, J. treemap InfoVis ResultMap system data visualisation digital libraries digital repositories hierarchical treemap representations online search interfaces query processing query string-driven digital library search engines repository search visualization search engines trees (mathematics) user interfaces IEEE Transactions on Visualization and Computer Graphics digital library digital repository evaluation infovis search engine search visualization treemap user study 2009 infovis09--5290711 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison. When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records. Marchand, G. Mukherjee, V. Plaisant, C. Roseman, D. Shneiderman, B. Smith, M. Spring, N. Wang, T.D. categorical filter InfoVis data visualisation human computer interaction interactive visualization technique temporal categorical searching temporal ordering temporal summaries IEEE Transactions on Visualization and Computer Graphics human-computer interaction information visualization interaction design temporal categorical data visualization 2009 infovis09--5290710 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data. Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns. Guo, D. clustering geographic graph hierarchy interaction network overview InfoVis cartography county-to-county migration data data visualisation geographic space integrated interactive visualization framework interactive flow mapping large spatial interaction data multivariate clustering multivariate visualization spatially constrained graph partitioning method user interfaces weighted location-to-location network IEEE Transactions on Visualization and Computer Graphics contiguity constraints coordinated views data mining flow mapping graph partitioning hierarchical clustering multidimensional visualization spatial interaction 2009 infovis09--5290709 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Comparing Dot and Landscape Spatializations for Visual Memory Differences. Spatialization displays use a geographic metaphor to arrange non-spatial data. For example, spatializations are commonly applied to document collections so that document themes appear as geographic features such as hills. Many common spatialization interfaces use a 3-D landscape metaphor to present data. However, it is not clear whether 3-D spatializations afford improved speed and accuracy for user tasks compared to similar 2-D spatializations. We describe a user study comparing users' ability to remember dot displays, 2-D landscapes, and 3-D landscapes for two different data densities (500 vs. 1000 points). Participants' visual memory was statistically more accurate when viewing dot displays and 3-D landscapes compared to 2-D landscapes. Furthermore, accuracy remembering a spatialization was significantly better overall for denser spatializations. Theseresults are of benefit to visualization designers who are contemplating the best ways to present data using spatialization techniques. Dreezer, R. Swindells, C. Tory, M. document geographic user study InfoVis 2D landscapes 3D landscapes data visualisation document collections document handling dot displays dot spatializations geographic metaphor landscape spatializations nonspatial data spatialization displays spatialization interfaces visual memory differences IEEE Transactions on Visualization and Computer Graphics evaluation / methodology information interfaces and presentation landscape visualization screen design software psychology user / machine systems 2009 infovis09--5290708 10/14/2009 IEEE Transactions on Visualization and Computer Graphics SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data. We present a case study of our experience designing SellTrend, a visualization system for analyzing airline travel purchase requests. The relevant transaction data can be characterized as multi-variate temporal and categorical event sequences, and the chief problem addressed is how to help company analysts identify complex combinations of transaction attributes that contribute to failed purchase requests. SellTrend combines a diverse set of techniques ranging from time series visualization to faceted browsing and historical trend analysis in order to help analysts make sense of the data. We believe that the combination of views and interaction capabilities in SellTrend provides an innovative approach to this problem and to other similar types of multivariate, temporally driven transaction data analysis. Initial feedback from company analysts confirms the utility and benefits of the system. Liu, Z. Stasko, J. Sullivan, T. case study categorical interaction time series InfoVis SellTrend airline travel purchase requests categorical event sequences data analysis data visualisation historical trend analysis inter-attribute visual analysis multi-variate temporal event sequences temporal transaction data analysis time series time series visualization travel industry IEEE Transactions on Visualization and Computer Graphics categorical data information visualization investigative analysis multiple attributes multiple views time series data transaction analysis 2009 infovis09--5290707 10/14/2009 IEEE Transactions on Visualization and Computer Graphics FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries. When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data. Conversy, S. Hurter, C. Tissoires, B. brushing InfoVis FromDaDy air traffic aircraft trajectories data visualisation iterative queries multidimensional data exploration pick and drop operation query processing trajectory visualization tool visual analysis IEEE Transactions on Visualization and Computer Graphics direct manipulation iterative exploration trajectories visualization 2009 infovis09--5290706 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations. While many data sets contain multiple relationships, depicting more than one data relationship within a single visualization is challenging. We introduce Bubble Sets as a visualization technique for data that has both a primary data relation with a semantically significant spatial organization and a significant set membership relation in which members of the same set are not necessarily adjacent in the primary layout. In order to maintain the spatial rights of the primary data relation, we avoid layout adjustment techniques that improve set cluster continuity and density. Instead, we use a continuous, possibly concave, isocontour to delineate set membership, without disrupting the primary layout. Optimizations minimize cluster overlap and provide for calculation of the isocontours at interactive speeds. Case studies show how this technique can be used to indicate multiple sets on a variety of common visualizations. Carpendale, S. Collins, C. Penn, G. cluster InfoVis Bubble sets data relationship data sets data visualisation data visualization isocontours primary data relation set cluster continuity set relation set theory IEEE Transactions on Visualization and Computer Graphics clustering graph visualization spatial layout tree visualization 2009 infovis09--5290705 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Scattering Points in Parallel Coordinates. In this paper, we present a novel parallel coordinates design integrated with points (scattering points in parallel coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated dimensional incremental multidimensional scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks. Guo, P. Qu, H. Xiao, H. Yuan, X. Zhou, H. brushing case study interaction multiple views parallel coordinates scatterplot InfoVis GPU data selection data visualisation data visualization dimensional incremental multidimensional scaling multidimensional scaling parallel coordinates scattering points visual analysis tasks IEEE Transactions on Visualization and Computer Graphics dimensionality reduction interactivity quality metrics variable ordering 2009 infovis09--5290704 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics. Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario. Johansson, J. Johansson, S. metrics InfoVis data reduction data visualisation interactive dimensionality reduction interactive systems multivariate data sets multivariate visualization technique quality metrics user-defined combinations IEEE Transactions on Visualization and Computer Graphics information visualization multidimensional scaling parallel coordinates scatterplot 2009 infovis09--5290703 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Visualizing Social Photos on a Hasse Diagram for Eliciting Relations and Indexing New Photos. Social photos, which are taken during family events or parties, represent individuals or groups of people. We show in this paper how a Hasse diagram is an efficient visualization strategy for eliciting different groups and navigating through them. However, we do not limit this strategy to these traditional uses. Instead we show how it can also be used for assisting in indexing new photos. Indexing consists of identifying the event and people in photos. It is an integral phase that takes place before searching and sharing. In our method we use existing indexed photos to index new photos. This is performed through a manual drag and drop procedure followed by a content fusion process that we call 'propagation'. At the core of this process is the necessity to organize and visualize the photos that will be used for indexing in a manner that is easily recognizable and accessible by the user. In this respect we make use of an object Galois sub-hierarchy and display it using a Hasse diagram. The need for an incremental display that maintains the user's mental map also leads us to propose a novel way of building the Hasse diagram. To validate the approach, we present some tests conducted with a sample of users that confirm the interest of this organization, visualization and indexation approach. Finally, we conclude by considering scalability, the possibility to extract social networks and automatically create personalised albums. Crampes, M. Ranwez, S. Villerd, J. de Oliveira-Kumar, J. hierarchy social InfoVis Galois fields Hasse diagram content fusion process data analysis data visualisation eliciting relations formal concept analysis human computer interaction indexing indexing new photos information visualization object Galois sub-hierarchy social networking (online) social photos visualization strategy IEEE Transactions on Visualization and Computer Graphics Galois sub-hierarchy formal concept analysis hasse diagram indexation information visualization social photos 2009 infovis09--5290702 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Configuring Hierarchical Layouts to Address Research Questions. We explore the effects of selecting alternative layouts in hierarchical displays that show multiple aspects of large multivariate datasets, including spatial and temporal characteristics. Hierarchical displays of this type condition a dataset by multiple discrete variable values, creating nested graphical summaries of the resulting subsets in which size, shape and colour can be used to show subset properties. These 'small multiples' are ordered by the conditioning variable values and are laid out hierarchically using dimensional stacking. Crucially, we consider the use of different layouts at different hierarchical levels, so that the coordinates of the plane can be used more effectively to draw attention to trends and anomalies in the data. We argue that these layouts should be informed by the type of conditioning variable and by the research question being explored. We focus on space-filling rectangular layouts that provide data-dense and rich overviews of data to address research questions posed in our exploratory analysis of spatial and temporal aspects of property sales in London. We develop a notation ('HiVE') that describes visualisation and layout states and provides reconfiguration operators, demonstrate its use for reconfiguring layouts to pursue research questions and provide guidelines for this process. We demonstrate how layouts can be related through animated transitions to reduce the cognitive load associated with their reconfiguration whilst supporting the exploratory process. Dykes, J. Slingsby, A. Wood, J. small multiples InfoVis cognitive load data visualisation dimensional stacking geography hierarchical displays hierarchical layouts multivariate datasets research questions space-filling rectangular layouts temporal databases visual databases IEEE Transactions on Visualization and Computer Graphics exploratory geovisualization guidelines hierarchical layout notation 2009 infovis09--5290701 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Smooth Graphs for Visual Exploration of Higher-Order State Transitions. In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities. Blaas, J. Botha, C. Grundy, E. Jones, M.W. Laramee, R.S. Post, F. clustering graph InfoVis biological data biology computing graph theory higher-order state transitions large observational time series smooth graphs splines splines (mathematics) state sequences state-graph exploration methods time series IEEE Transactions on Visualization and Computer Graphics biological data graph drawing state transitions time series 2009 infovis09--5290700 10/14/2009 IEEE Transactions on Visualization and Computer Graphics A Comparison of User-Generated and Automatic Graph Layouts. The research presented in this paper compares user-generated and automatic graph layouts. Following the methods suggested by van Ham et al. (2008), a group of users generated graph layouts using both multi-touch interaction on a tabletop display and mouse interaction on a desktop computer. Users were asked to optimize their layout for aesthetics and analytical tasks with a social network. We discuss characteristics of the user-generated layouts and interaction methods employed by users in this process. We then report on a web-based study to compare these layouts with the output of popular automatic layout algorithms. Our results demonstrate that the best of the user-generated layouts performed as well as or better than the physics-based layout. Orthogonal and circular automatic layouts were found to be considerably less effective than either the physics-based layout or the best of the user-generated layouts. We highlight several attributes of the various layouts that led to high accuracy and improved task completion time, as well as aspects in which traditional automatic layout methods were unsuccessful for our tasks. Dwyer, T. Fisher, D. Isenberg, P. Lee, B. North, C. Quinn, K.I. Robertson, G. aesthetics graph interaction network social InfoVis automatic graph layout circular automatic layouts desktop computer graph theory mouse interaction multitouch interaction orthogonal automatic layouts physics-based layout social networking (online) tabletop display user interfaces user-generated layouts IEEE Transactions on Visualization and Computer Graphics automatic layout algorithms graph layout graph-drawing aesthetics network layout user-generated layout 2009 infovis09--5290699 10/14/2009 IEEE Transactions on Visualization and Computer Graphics ?Search, Show Context, Expand on Demand?: Supporting Large Graph Exploration with Degree-of-Interest. A common goal in graph visualization research is the design of novel techniques for displaying an overview of an entire graph. However, there are many situations where such an overview is not relevant or practical for users, as analyzing the global structure may not be related to the main task of the users that have semi-specific information needs. Furthermore, users accessing large graph databases through an online connection or users running on less powerful (mobile) hardware simply do not have the resources needed to compute these overviews. In this paper, we advocate an interaction model that allows users to remotely browse the immediate context graph around a specific node of interest. We show how Furnas' original degree of interest function can be adapted from trees to graphs and how we can use this metric to extract useful contextual subgraphs, control the complexity of the generated visualization and direct users to interesting datapoints in the context. We demonstrate the effectiveness of our approach with an exploration of a dense online database containing over 3 million legal citations. Perer, A. van Ham, F. database graph hardware interaction overview InfoVis citation analysis contextual subgraph data visualisation dense online database graph theory graph visualization immediate context graph large graph database large graph exploration legal citation mathematics computing IEEE Transactions on Visualization and Computer Graphics degree of interest focus+context graph visualization legal citation networks network visualization 2009 infovis09--5290698 10/14/2009 IEEE Transactions on Visualization and Computer Graphics ActiviTree: Interactive Visual Exploration of Sequences in Event-Based Data Using Graph Similarity. The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of today's information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for Web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines. Cooper, M. Johansson, J. Vrotsou, K. data mining graph social InfoVis ActiviTree Web searching algorithmic search mechanisms complex event-based temporal data computational complexity data mining event-based data graph similarity graph theory interactive visual data mining interactive visual sequence exploration IEEE Transactions on Visualization and Computer Graphics event-based data graph similarity interactive visual exploration node similarity sequence identification 2009 infovis09--5290697 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations. We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands for translation, rotation, scaling, or modifying graphical properties (such as opacity) and layout patterns can be performed by using a hotbox (a transiently popped-up, semi-transparent set of widgets) that has been extended in novel ways to integrate specification of commands with 1D or 2D arguments. Our techniques require only one mouse button and one keyboard key, and are designed for fast, gestural, in-place interaction. We present the design and integration of these interaction techniques, and illustrate their use in interactive graph visualization. Our techniques are implemented in NAViGaTOR, a software package for visualizing and analyzing biological networks. An initial usability study is also reported. Jurisica, I. McGuffin, M.J. graph interaction network radial usability InfoVis NAViGaTOR biological networks biology computing data visualisation graph theory interaction techniques interactive graph visualization interactive systems lasso selection network visualizations selecting subgraphs software package usability study IEEE Transactions on Visualization and Computer Graphics biological networks hotbox interactive graph drawing marking menus network layout radial menu 2009 infovis09--5290696 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Conjunctive Visual Forms. Visual exploration of multidimensional data is a process of isolating and extracting relationships within and between dimensions. Coordinated multiple view approaches are particularly effective for visual exploration because they support precise expression of heterogeneous multidimensional queries using simple interactions. Recent visual analytics research has made significant progress in identifying and understanding patterns of composed views and coordinations that support fast, flexible, and open-ended data exploration. What is missing is formalization of the space of expressible queries in terms of visual representation and interaction. This paper introduces the conjunctive visual form model in which visual exploration consists of interactively-driven sequences of transitions between visual states that correspond to conjunctive normal forms in boolean logic. The model predicts several new and useful ways to extend the space of rapidly expressible queries through addition of simple interactive capabilities to existing compositional patterns. Two recent related visual tools offer a subset of these capabilities, providing a basis for conjecturing about such extensions. Weaver, C. interaction visual analytics InfoVis boolean logic conjunctive visual form model data visualisation heterogeneous multidimensional queries multidimensional data visual exploration query processing visual representation IEEE Transactions on Visualization and Computer Graphics boolean query brushing conjunctive normal form exploratory visualization multiple views visual abstraction 2009 infovis09--5290695 10/14/2009 IEEE Transactions on Visualization and Computer Graphics A Nested Model for Visualization Design and Validation. We present a nested model for the visualization design and validation with four layers: characterize the task and data in the vocabulary of the problem domain, abstract into operations and data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We also provide three recommendations motivated by this model: authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization. Munzner, T. evaluation interaction InfoVis data visualisation domain characterization nested process model visual encoding visualization design IEEE Transactions on Visualization and Computer Graphics design evaluation framework models 2009 infovis09--5290694 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Spatiotemporal Analysis of Sensor Logs using Growth Ring Maps. Spatiotemporal analysis of sensor logs is a challenging research field due to three facts: a) traditional two-dimensional maps do not support multiple events to occur at the same spatial location, b) three-dimensional solutions introduce ambiguity and are hard to navigate, and c) map distortions to solve the overlap problem are unfamiliar to most users. This paper introduces a novel approach to represent spatial data changing over time by plotting a number of non-overlapping pixels, close to the sensor positions in a map. Thereby, we encode the amount of time that a subject spent at a particular sensor to the number of plotted pixels. Color is used in a twofold manner; while distinct colors distinguish between sensor nodes in different regions, the colors' intensity is used as an indicator to the temporal property of the subjects' activity. The resulting visualization technique, called growth ring maps, enables users to find similarities and extract patterns of interest in spatiotemporal data by using humans' perceptual abilities. We demonstrate the newly introduced technique on a dataset that shows the behavior of healthy and Alzheimer transgenic, male and female mice. We motivate the new technique by showing that the temporal analysis based on hierarchical clustering and the spatial analysis based on transition matrices only reveal limited results. Results and findings are cross-validated using multidimensional scaling. While the focus of this paper is to apply our visualization for monitoring animal behavior, the technique is also applicable for analyzing data, such as packet tracing, geographic monitoring of sales development, or mobile phone capacity planning. Bak, P. Janetzko, H. Keim, D.A. Mansmann, F. clustering color geographic InfoVis Alzheimer transgenic mice biology computing biosensors colors intensity colour graphics data loggers data visualisation distinct colors geographic monitoring growth ring maps hierarchical clustering map distortions mobile phone capacity planning multidimensional scaling nonoverlapping pixels packet tracing sensor logs spatiotemporal analysis spatial data visualization technique IEEE Transactions on Visualization and Computer Graphics animal behavior dense pixel displays spatiotemporal visualization visual analytics 2009 infovis09--5290693 10/14/2009 IEEE Transactions on Visualization and Computer Graphics GeneShelf: A Web-based Visual Interface for Large Gene Expression Time-Series Data Repositories. A widespread use of high-throughput gene expression analysis techniques enabled the biomedical research community to share a huge body of gene expression datasets in many public databases on the web. However, current gene expression data repositories provide static representations of the data and support limited interactions. This hinders biologists from effectively exploring shared gene expression datasets. Responding to the growing need for better interfaces to improve the utility of the public datasets, we have designed and developed a new web-based visual interface entitled GeneShelf (http://bioinformatics.cnmcresearch.org/GeneShelf). It builds upon a zoomable grid display to represent two categorical dimensions. It also incorporates an augmented timeline with expandable time points that better shows multiple data values for the focused time point by embedding bar charts. We applied GeneShelf to one of the largest microarray datasets generated to study the progression and recovery process of injuries at the spinal cord of mice and rats. We present a case study and a preliminary qualitative user study with biologists to show the utility and usability of GeneShelf. Hoffman, E. Kim, B. Knoblach, S. Lee, B. Seo, J. bioinformatics case study categorical usability user study InfoVis GeneShelf Internet Web-based visual interface bar charts biology computing data structures genetics large gene expression time-series data repositories public databases static data representations user interfaces IEEE Transactions on Visualization and Computer Graphics animation augmented timeline bioinformatics visualization gene expression profiling zoomable grid 2009 infovis09--5290692 10/14/2009 IEEE Transactions on Visualization and Computer Graphics MizBee: A Multiscale Synteny Browser. In the field of comparative genomics, scientists seek to answer questions about evolution and genomic function by comparing the genomes of species to find regions of shared sequences. Conserve dsyntenic blocks are an important biological data abstraction for indicating regions of shared sequences. The goal of this work is to show multiple types of relationships at multiple scales in a way that is visually comprehensible in accordance with known perceptual principles. We present a task analysis for this domain where the fundamental questions asked by biologists can be understood by a characterization of relationships into the four types of proximity/location, size, orientation, and similarity/strength, and the four scales of genome, chromosome, block, and genomic feature. We also propose a new taxonomy of the design space for visually encoding conservation data. We present MizBee, a multiscale synteny browser with the unique property of providing interactive side-by-side views of the data across the range of scales supporting exploration of all of these relationship types. We conclude with case studies from two biologists who used MizBee to augment their previous automatic analysis work flow, providing anecdotal evidence about the efficacy of the system for the visualization of syntenic data, the analysis of conservation relationships, and the communication of scientific insights. Meyer, M. Munzner, T. Pfister, H. taxonomy InfoVis MizBee biological data abstraction biology computing chromosome comparative genomics data structures data visualisation data visualization dsyntenic blocks genome genomics multiscale synteny browser IEEE Transactions on Visualization and Computer Graphics bioinformatics design study information visualization synteny 2009 infovis09--5290691 10/14/2009 IEEE Transactions on Visualization and Computer Graphics Constructing Overview + Detail Dendrogram-Matrix Views. A dendrogram that visualizes a clustering hierarchy is often integrated with a re-orderable matrix for pattern identification. The method is widely used in many research fields including biology, geography, statistics, and data mining. However, most dendrograms do not scale up well, particularly with respect to problems of graphical and cognitive information overload. This research proposes a strategy that links an overview dendrogram and a detail-view dendrogram, each integrated with a re-orderable matrix. The overview displays only a user-controlled, limited number of nodes that represent the ldquoskeletonrdquo of a hierarchy. The detail view displays the sub-tree represented by a selected meta-node in the overview. The research presented here focuses on constructing a concise overview dendrogram and its coordination with a detail view. The proposed method has the following benefits: dramatic alleviation of information overload, enhanced scalability and data abstraction quality on the dendrogram, and the support of data exploration at arbitrary levels of detail. The contribution of the paper includes a new metric to measure the ldquoimportancerdquo of nodes in a dendrogram; the method to construct the concise overview dendrogram from the dynamically-identified, important nodes; and measure for evaluating the data abstraction quality for dendrograms. We evaluate and compare the proposed method to some related existing methods, and demonstrating how the proposed method can help users find interesting patterns through a case study on county-level U.S. cervical cancer mortality and demographic data. Chen, J. MacEachren, A.M. Peuquet, D.J. case study clustering data mining hierarchy matrix overview statistics InfoVis clustering hierarchy data abstraction quality data exploration data structures dendrogram-matrix views detail view dendrogram matrix algebra overview dendrogram pattern clustering pattern identification reorderable matrix IEEE Transactions on Visualization and Computer Graphics compound graph data abstraction quality metrics dendrogram hierarchical clusters reorderable matrix 2009 infovis09--5290690 10/14/2009 IEEE Transactions on Visualization and Computer Graphics ABySS-Explorer: Visualizing Genome Sequence Assemblies. One bottleneck in large-scale genome sequencing projects is reconstructing the full genome sequence from the short subsequences produced by current technologies. The final stages of the genome assembly process inevitably require manual inspection of data inconsistencies and could be greatly aided by visualization. This paper presents our design decisions in translating key data features identified through discussions with analysts into a concise visual encoding. Current visualization tools in this domain focus on local sequence errors making high-level inspection of the assembly difficult if not impossible. We present a novel interactive graph display, ABySS-Explorer, that emphasizes the global assembly structure while also integrating salient data features such as sequence length. Our tool replaces manual and in some cases pen-and-paper based analysis tasks, and we discuss how user feedback was incorporated into iterative design refinements. Finally, we touch on applications of this representation not initially considered in our design phase, suggesting the generality of this encoding for DNA sequence data. Birol, I. Jackman, S.D. Jones, S.J.M. Nielsen, C.B. graph InfoVis ABySS-Explorer DNA DNA sequence data bioinformatics bioinformatics visualization data visualisation genome assembly process genome sequence assemblies genome sequencing projects genomics interactive graph display interactive systems pen-and-paper based analysis tasks user feedback visualization tools IEEE Transactions on Visualization and Computer Graphics DNA sequence bioinformatics visualization design study genome assembly 2009 infovis10--180 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Necklace Maps. Statistical data associated with geographic regions is nowadays globally available in large amounts and hence automated methods to visually display these data are in high demand. There are several well-established thematic map types for quantitative data on the ratio-scale associated with regions: choropleth maps, cartograms, and proportional symbol maps. However, all these maps suffer from limitations, especially if large data values are associated with small regions. To overcome these limitations, we propose a novel type of quantitative thematic map, the necklace map. In a necklace map, the regions of the underlying two-dimensional map are projected onto intervals on a one-dimensional curve (the necklace) that surrounds the map regions. Symbols are scaled such that their area corresponds to the data of their region and placed without overlap inside the corresponding interval on the necklace. Necklace maps appear clear and uncluttered and allow for comparatively large symbol sizes. They visualize data sets well which are not proportional to region sizes. The linear ordering of the symbols along the necklace facilitates an easy comparison of symbol sizes. One map can contain several nested or disjoint necklaces to visualize clustered data. The advantages of necklace maps come at a price: the association between a symbol and its region is weaker than with other types of maps. Interactivity can help to strengthen this association if necessary. We present an automated approach to generate necklace maps which allows the user to interactively control the final symbol placement. We validate our approach with experiments using various data sets and maps. Speckmann, B. Verbeek, K. geographic InfoVis IEEE Transactions on Visualization and Computer Graphics Necklace Maps automated cartography geographic visualization proportional symbol maps 2010 infovis10--191 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Rethinking Map Legends with Visualization. This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set ofguidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. Theseare demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographicliterature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches tolegend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamismand design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphicand The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specificneeds, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements offact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence ofthis work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization. Dykes, J. Slingsby, A. Wood, J. education evaluation InfoVis IEEE Transactions on Visualization and Computer Graphics cartography design digimap service legend online web mapping visualization 2010 infovis10--193 10/27/2010 IEEE Transactions on Visualization and Computer Graphics SignalLens: Focus+Context Applied to Electronic Time Series. Electronic test and measurement systems are becoming increasingly sophisticated in order to match the increased complexity and ultra-high speed of the devices under test. A key feature in many such instruments is a vastly increased capacity for storage of digital signals. Storage of 109 time points or more is now possible. At the same time, the typical screens on such measurement devices are relatively small. Therefore, these instruments can only render an extremely small fraction of the complete signal at any time. SignalLens uses a Focus+Context approach to provide a means of navigating to and inspecting low-level signal details in the context of the entire signal trace. This approach provides a compact visualization suitable for embedding into the small displays typically provided by electronic measurement instruments. We further augment this display with computed tracks which display time-aligned computed properties of the signal. By combining and filtering these computed tracks it is possible to easily and quickly find computationally detected features in the data which are often obscured by the visual compression required to render the large data sets on a small screen. Further, these tracks can be viewed in the context of the entire signal trace as well as visible high-level signal features. Several examples using real-world electronic measurement data are presented, which demonstrate typical use cases and the effectiveness of the design. Kincaid, R. focus+context time series InfoVis IEEE Transactions on Visualization and Computer Graphics electronic signal focus+context lens signal processing test and measurement 2010 infovis10--137 10/27/2010 IEEE Transactions on Visualization and Computer Graphics MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data. Cells in an organism share the same genetic information in their DNA, but have very different forms and behavior because of the selective expression of subsets of their genes. The widely used approach of measuring gene expression over time from a tissue sample using techniques such as microarrays or sequencing do not provide information about the spatial position with in the tissue where these genes are expressed. In contrast, we are working with biologists who use techniques that measure gene expression in every individual cell of entire fruitfly embryos over an hour of their development, and do so for multiple closely-related subspecies of Drosophila. These scientists are faced with the challenge of integrating temporal gene expression data with the spatial location of cells and, moreover, comparing this data across multiple related species. We have worked with these biologists over the past two years to develop MulteeSum, a visualization system that supports inspection and curation of data sets showing gene expression over time, in conjunction with the spatial location of the cells where the genes are expressed - it is the first tool to support comparisons across multiple such data sets. MulteeSum is part of a general and flexible framework we developed with our collaborators that is built around multiple summaries for each cell, allowing the biologists to explore the results of computations that mix spatial information, gene expression measurements over time, and data from multiple related species or organisms. We justify our design decisions based on specific descriptions of the analysis needs of our collaborators, and provide anecdotal evidence of the efficacy of MulteeSum through a series of case studies. DePace, A. Meyer, M. Munzner, T. Pfister, H. InfoVis IEEE Transactions on Visualization and Computer Graphics gene expression spatial data temporal data 2010 infovis10--163 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Gremlin: An Interactive Visualization Model for Analyzing Genomic Rearrangements. In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements. Laidlaw, D.H. O'Brien, T.M. Raphael, B.J. Ritz, A.M. evaluation insight uncertainty user study InfoVis IEEE Transactions on Visualization and Computer Graphics bioinformatics information visualization insight-based evaluation 2010 infovis10--162 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Graphical Perception of Multiple Time Series. Line graphs have been the visualization of choice for temporal data ever since the days of William Playfair (1759-1823), but realistic temporal analysis tasks often include multiple simultaneous time series. In this work, we explore user performance for comparison, slope, and discrimination tasks for different line graph techniques involving multiple time series. Our results show that techniques that create separate charts for each time series--such as small multiples and horizon graphs--are generally more efficient for comparisons across time series with a large visual span. On the other hand, shared-space techniques--like standard line graphs--are typically more efficient for comparisons over smaller visual spans where the impact of overlap and clutter is reduced. Elmqvist, N. Javed, W. McDonnel, B. graph perception small multiples time series InfoVis IEEE Transactions on Visualization and Computer Graphics braided graphs design guidelines evaluation horizon graphs line graphs small multiples stacked graphs 2010 infovis10--209 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Uncovering Strengths and Weaknesses of Radial Visualizations---an Empirical Approach. Radial visualizations play an important role in the information visualization community. But the decision to choose a radial coordinate system is rather based on intuition than on scientific foundations. The empirical approach presented in this paper aims at uncovering strengths and weaknesses of radial visualizations by comparing them to equivalent ones in Cartesian coordinate systems. We identified memorizing positions of visual elements as a generic task when working with visualizations. A first study with 674 participants provides a broad data spectrum for exploring differences between the two visualization types. A second, complementing study with fewer participants focuses on further questions raised by the first study. Our findings document that Cartesian visualizations tend to outperform their radial counterparts especially with respect to answer times. Nonetheless, radial visualization seem to be more appropriate for focusing on a particular data dimension. Beck, F. Burch, M. Diehl, S. document radial InfoVis IEEE Transactions on Visualization and Computer Graphics radial visualization user study visual memory 2010 infovis10--164 10/27/2010 IEEE Transactions on Visualization and Computer Graphics How Information Visualization Novices Construct Visualizations. It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process. Grammel, L. Storey, M. Tory, M. visual analytics InfoVis IEEE Transactions on Visualization and Computer Graphics empirical study novices visual analytics visual mapping visualization visualization construction 2010 infovis10--149 10/27/2010 IEEE Transactions on Visualization and Computer Graphics eSeeTrack - Visualizing Sequential Fixation Patterns. We introduce eSeeTrack, an eye-tracking visualization prototype that facilitates exploration and comparison of sequential gaze orderings in a static or a dynamic scene. It extends current eye-tracking data visualizations by extracting patterns of sequential gaze orderings, displaying these patterns in a way that does not depend on the number of fixations on a scene, and enabling users to compare patterns from two or more sets of eye-gaze data. Extracting such patterns was very difficult with previous visualization techniques. eSeeTrack combines a timeline and a tree-structured visual representation to embody three aspects of eye-tracking data that users are interested in: duration, frequency and orderings of fixations. We demonstrate the usefulness of eSeeTrack via two case studies on surgical simulation and retail store chain data. We found that eSeeTrack allows ordering of fixations to be rapidly queried, explored and compared. Furthermore, our tool provides an effective and efficient mechanism to determine pattern outliers. This approach can be effective for behavior analysis in a variety of domains that are described at the end of this paper. Swindells, C. Tory, M. Tsang, H.Y. InfoVis IEEE Transactions on Visualization and Computer Graphics eye-tracking fixation pattern timeline tree-structured visualization 2010 infovis10--150 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization. Pixel-based visualization is a popular method of conveying large amounts of numerical data graphically. Application scenarios include business and finance, bioinformatics and remote sensing. In this work, we examined how the usability of such visual representations varied across different tasks and block resolutions. The main stimuli consisted of temporal pixel-based visualization with a white-red color map, simulating monthly temperature variation over a six-year period. In the first study, we included 5 separate tasks to exert different perceptual loads. We found that performance varied considerably as a function of task, ranging from 75% correct in low-load tasks to below 40% in high-load tasks. There was a small but consistent effect of resolution, with the uniform patch improving performance by around 6% relative to higher block resolution. In the second user study, we focused on a high-load task for evaluating month-to-month changes across different regions of the temperature range. We tested both CIE L*u*v* and RGB color spaces. We found that the nature of the change-evaluation errors related directly to the distance between the compared regions in the mapped color space. We were able to reduce such errors by using multiple color bands for the same data range. In a final study, we examined more fully the influence of block resolution on performance, and found block resolution had a limited impact on the effectiveness of pixel-based visualization. Borgo, R. Chen, M. Janicke, H. Murray, T. Proctor, K. Thornton, I.M. bioinformatics business color evaluation pixel usability user study InfoVis IEEE Transactions on Visualization and Computer Graphics change detection evaluation pixel-based visualization user study visual search 2010 infovis10--161 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Graphical inference for infovis. How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure. Buja, A. Cook, D. Hofmann, H. Wickham, H. statistics uncertainty InfoVis IEEE Transactions on Visualization and Computer Graphics data plot null hypotheses permutation tests statistics visual testing 2010 infovis10--176 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Matching Visual Saliency to Confidence in Plots of Uncertain Data. Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets. Feng, D. Kwock, L. Lee, Y. Taylor, R.M. brushing parallel coordinates uncertainty InfoVis IEEE Transactions on Visualization and Computer Graphics brushing multivariate data parallel coordinates scatterplot uncertainty visualization 2010 infovis10--186 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Perceptual Guidelines for Creating Rectangular Treemaps. Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hierarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the compared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution of rectangle aspect ratios within a treemap should include non-squares, but should avoid extremes. We then compare treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps and suggest alternate approaches for treemap layout. Agrawala, M. Heer, J. Kong, N. treemap InfoVis IEEE Transactions on Visualization and Computer Graphics Mechanical Turk experiment graphical perception rectangular area treemap visual encoding visualization 2010 infovis10--177 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Mental Models, Visual Reasoning and Interaction in Information Visualization: A Top-down Perspective. Although previous research has suggested that examining the interplay between internal and external representations can benefit our understanding of the role of information visualization (InfoVis) in human cognitive activities, there has been little work detailing the nature of internal representations, the relationship between internal and external representations and how interaction is related to these representations. In this paper, we identify and illustrate a specific kind of internal representation, mental models, and outline the high-level relationships between mental models and external visualizations. We present a top-down perspective of reasoning as model construction and simulation, and discuss the role of visualization in model based reasoning. From this perspective, interaction can be understood as active modeling for three primary purposes: external anchoring, information foraging, and cognitive offloading. Finally we discuss the implications of our approach for design, evaluation and theory development. Liu, Z. Stasko, J. evaluation interaction theory InfoVis IEEE Transactions on Visualization and Computer Graphics distributed cognition information visualization interaction mental model model-based reasoning theory 2010 infovis10--174 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Laws of Attraction: From Perceptual Forces to Conceptual Similarity. Many of the pressing questions in information visualization deal with how exactly a user reads a collection of visual marks as information about relationships between entities. Previous research has suggested that people see parts of a visualization as objects, and may metaphorically interpret apparent physical relationships between these objects as suggestive of data relationships. We explored this hypothesis in detail in a series of user experiments. Inspired by the concept of implied dynamics in psychology, we first studied whether perceived gravity acting on a mark in a scatterplot can lead to errors in a participant's recall of the mark's position. The results of this study suggested that such position errors exist, but may be more strongly influenced by attraction between marks. We hypothesized that such apparent attraction may be influenced by elements used to suggest relationship between objects, such as connecting lines, grouping elements, and visual similarity. We further studied what visual elements are most likely to cause this attraction effect, and whether the elements that best predicted attraction errors were also those which suggested conceptual relationships most strongly. Our findings show a correlation between attraction errors and intuitions about relatedness, pointing towards a possible mechanism by which the perception of visual marks becomes an interpretation of data relationships. Kosara, R. Ziemkiewicz, C. perception scatterplot InfoVis IEEE Transactions on Visualization and Computer Graphics cognition theory laboratory study perceptual cognition visualization models 2010 infovis10--184 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Pargnostics: Screen-Space Metrics for Parallel Coordinates. Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user's preferences based on our metrics and model. Dasgupta, A. Kosara, R. matrix metrics parallel coordinates InfoVis IEEE Transactions on Visualization and Computer Graphics display optimization metrics parallel coordinates visualization models 2010 infovis10--138 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Comparative Analysis of Multidimensional, Quantitative Data. When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions. Kashofer, K. Lex, A. Partl, C. Schmalstieg, D. Streit, M. cluster clustering color filter parallel coordinates InfoVis IEEE Transactions on Visualization and Computer Graphics bioinformatics visualization cluster comparison multidimensional data 2010 infovis10--130 10/27/2010 IEEE Transactions on Visualization and Computer Graphics An Extension of Wilkinson's Algorithm for Positioning Tick Labels on Axes. The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson's optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization. Hanrahan, P. Lin, S. Talbot, J. metrics InfoVis IEEE Transactions on Visualization and Computer Graphics axis labeling nice numbers 2010 infovis10--197 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Stacking Graphic Elements to Avoid Over-Plotting. An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color(heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightnesshave relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link toindividual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays. Anand, A. Dang, T.N. Wilkinson, L. color pixel InfoVis IEEE Transactions on Visualization and Computer Graphics density-based visualization dot plots multidimensional data parallel coordinate plots 2010 infovis10--216 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Visualization of Diversity in Large Multivariate Data Sets. Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity. Hess, R. Ju, C. Metoyer, R. Pham, T. Zhang, E. evaluation machine learning user study InfoVis IEEE Transactions on Visualization and Computer Graphics categorical data diversity evaluation information visualization multivariate data 2010 infovis10--185 10/27/2010 IEEE Transactions on Visualization and Computer Graphics PedVis: A Structured, Space-Efficient Technique for Pedigree Visualization. Public genealogical databases are becoming increasingly populated with historical data and records of the current population's ancestors. As this increasing amount of available information is used to link individuals to their ancestors, the resulting trees become deeper and more dense, which justifies the need for using organized, space-efficient layouts to display the data. Existing layouts are often only able to show a small subset of the data at a time. As a result, it is easy to become lost when navigating through the data or to lose sight of the overall tree structure. On the contrary, leaving space for unknown ancestors allows one to better understand the tree's structure, but leaving this space becomes expensive and allows fewer generations to be displayed at a time. In this work, we propose that the H-tree based layout be used in genealogical software to display ancestral trees. We will show that this layout presents an increase in the number of displayable generations, provides a nicely arranged, symmetrical, intuitive and organized fractal structure, increases the user's ability to understand and navigate through the data, and accounts for the visualization requirements necessary for displaying such trees. Finally, user-study results indicate potential for user acceptance of the new layout. Nonato, L.G. Silva, C.T. Tuttle, C. InfoVis IEEE Transactions on Visualization and Computer Graphics H-Tree genealogy pedigree 2010 infovis10--159 10/27/2010 IEEE Transactions on Visualization and Computer Graphics GeneaQuilts: A System for Exploring Large Genealogies. GeneaQuilts is a new visualization technique for representing large genealogies of up to several thousand individuals. The visualization takes the form of a diagonally-filled matrix, where rows are individuals and columns are nuclear families. After identifying the major tasks performed in genealogical research and the limits of current software, we present an interactive genealogy exploration system based on GeneaQuilts. The system includes an overview, a timeline, search and filtering components, and a new interaction technique called Bring & Slide that allows fluid navigation in very large genealogies. We report on preliminary feedback from domain experts and show how our system supports a number of their tasks. Bae, J. Bezerianos, A. Dragicevic, P. Fekete, J.-D. Watson, B. interaction matrix navigation overview InfoVis IEEE Transactions on Visualization and Computer Graphics genealogy visualization interaction 2010 infovis10--217 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Visualization of Graph Products. Graphs are a versatile structure and abstraction for binary relationships between objects. To gain insight into such relationships, their corresponding graph can be visualized. In the past, many classes of graphs have been defined, e.g. trees, planar graphs, directed acyclic graphs, and visualization algorithms were proposed for these classes. Although many graphs may only be classified as "general" graphs, they can contain substructures that belong to a certain class. Archambault proposed the TopoLayout framework: rather than draw any arbitrary graph using one method, split the graph into components that are homogeneous with respect to one graph class and then draw each component with an algorithm best suited for this class. Graph products constitute a class that arises frequently in graph theory, but for which no visualization algorithm has been proposed until now. In this paper, we present an algorithm for drawing graph products and the aesthetic criterion graph product's drawings are subject to. We show that the popular High-Dimensional Embedder approach applied to cartesian products already respects this aestetic criterion, but has disadvantages. We also present how our method is integrated as a new component into the TopoLayout framework. Our implementation is used for further research of graph products in a biological context. Heine, C. Hellmuth, M. Janicke, S. Scheuermann, G. Stadler, P.F. graph insight theory InfoVis IEEE Transactions on Visualization and Computer Graphics TopoLayout graph drawing graph products 2010 infovis10--210 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Untangling Euler Diagrams. In many common data analysis scenarios the data elements are logically grouped into sets. Venn and Euler style diagrams are a common visual representation of such set membership where the data elements are represented by labels or glyphs and sets are indicated by boundaries surrounding their members. Generating such diagrams automatically such that set regions do not intersect unless the corresponding sets have a non-empty intersection is a difficult problem. Further, it may be impossible in some cases if regions are required to be continuous and convex. Several approaches exist to draw such set regions using more complex shapes, however, the resulting diagrams can be difficult to interpret. In this paper we present two novel approaches for simplifying a complex collection of intersecting sets into a strict hierarchy that can be more easily automatically arranged and drawn (Figure 1). In the first approach, we use compact rectangular shapes for drawing each set, attempting to improve the readability of the set intersections. In the second approach, we avoid drawing intersecting set regions by duplicating elements belonging to multiple sets. We compared both of our techniques to the traditional non-convex region technique using five readability tasks. Our results show that the compact rectangular shapes technique was often preferred by experimental subjects even though the use of duplications dramatically improves the accuracy and performance time for most of our tasks. In addition to general set representation our techniques are also applicable to visualization of networks with intersecting clusters of nodes. Dwyer, T. Henry Riche, N. hierarchy InfoVis IEEE Transactions on Visualization and Computer Graphics Euler diagrams graph visualization information visualization set visualization 2010 infovis10--205 10/27/2010 IEEE Transactions on Visualization and Computer Graphics The FlowVizMenu and Parallel Scatterplot Matrix: Hybrid Multidimensional Visualizations for Network Exploration. A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up transiently and manipulated with rapid, fluid gestures to select and modify the axes of its scatterplot. Second, the FlowVizMenu can be used to steer an attribute-driven layout of the network, causing certain nodes of a node-link diagram to move toward their corresponding positions in a scatterplot while others can be positioned manually or by force-directed layout. Third, we describe a novel hybrid approach that combines a scatterplot matrix (SPLOM) and parallel coordinates called the Parallel Scatterplot Matrix (P-SPLOM), which can be used to visualize and select features within the network. We also describe a novel arrangement of scatterplots called the Scatterplot Staircase (SPLOS) that requires less space than a traditional scatterplot matrix. Initial user feedback is reported. Chiricota, Y. Jurisica, I. McGuffin, M.J. Viau, C. graph matrix metrics network parallel coordinates radial scatterplot InfoVis IEEE Transactions on Visualization and Computer Graphics attribute-driven layout interactive graph drawing network layout parallel coordinates radial menu scatterplot matrix 2010 infovis10--183 10/27/2010 IEEE Transactions on Visualization and Computer Graphics OpinionSeer: Interactive Visualization of Hotel Customer Feedback. The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinions on the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on building a successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze a large collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique that considers uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to convey customer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, we introduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate the effectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinions of different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customer opinions on other products or services. Au, N. Cui, W. Liu, S. Qu, H. Wei, F. Wu, Y. Zhou, H. business radial uncertainty InfoVis IEEE Transactions on Visualization and Computer Graphics opinion visualization radial visualization uncertainty visualization 2010 infovis10--206 10/27/2010 IEEE Transactions on Visualization and Computer Graphics The Streams of Our Lives: Visualizing Listening Histories in Context. The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a laband a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research. Baur, D. Boring, S. Sedlmair, M. Seiffert, F. design study overview statistics InfoVis IEEE Transactions on Visualization and Computer Graphics calendars design study information visualization lifelogging listening history music photos timeline 2010 infovis10--129 10/27/2010 IEEE Transactions on Visualization and Computer Graphics A Visual Backchannel for Large-Scale Events. We introduce the concept of a Visual Backchannel as a novel way of following and exploring online conversations about large-scale events. Microblogging communities, such as Twitter, are increasingly used as digital backchannels for timely exchange of brief comments and impressions during political speeches, sport competitions, natural disasters, and other large events. Currently, shared updates are typically displayed in the form of a simple list, making it difficult to get an overview of the fast-paced discussions as it happens in the moment and how it evolves over time. In contrast, our Visual Backchannel design provides an evolving, interactive, and multi-faceted visual overview of large-scale ongoing conversations on Twitter. To visualize a continuously updating information stream, we include visual saliency for what is happening now and what has just happened, set in the context of the evolving conversation. As part of a fully web-based coordinated-view system we introduce Topic Streams, a temporally adjustable stacked graph visualizing topics over time, a People Spiral representing participants and their activity, and an Image Cloud encoding the popularity of event photos by size. Together with a post listing, these mutually linked views support cross-filtering along topics, participants, and time ranges. We discuss our design considerations, in particular with respect to evolving visualizations of dynamically changing data. Initial feedback indicates significant interest and suggests several unanticipated uses. Carpendale, S. Dork, M. Gruen, D. Williamson, C. graph overview InfoVis IEEE Transactions on Visualization and Computer Graphics World Wide Web backchannel events information retrieval information visualization microblogging multiple views 2010 infovis10--179 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Narrative Visualization: Telling Stories with Data. Data visualization is regularly promoted for its ability to reveal stories within data, yet these "data stories" differ in important ways from traditional forms of storytelling. Storytellers, especially online journalists, have increasingly been integrating visualizations into their narratives, in some cases allowing the visualization to function in place of a written story. In this paper, we systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration). Our framework suggests design strategies for narrative visualization, including promising under-explored approaches to journalistic storytelling and educational media. Heer, J. Segel, E. InfoVis IEEE Transactions on Visualization and Computer Graphics case study design methods journalism narrative visualization social data analysis storytelling 2010 infovis10--144 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Declarative Language Design for Interactive Visualization. We investigate the design of declarative, domain-specific languages for constructing interactive visualizations. By separating specification from execution, declarative languages can simplify development, enable unobtrusive optimization, and support retargeting across platforms. We describe the design of the Protovis specification language and its implementation within an object-oriented, statically-typed programming language (Java). We demonstrate how to support rich visualizations without requiring a toolkit-specific data model and extend Protovis to enable declarative specification of animated transitions. To support cross-platform deployment, we introduce rendering and event-handling infrastructures decoupled from the runtime platform, letting designers retarget visualization specifications (e.g., from desktop to mobile phone) with reduced effort. We also explore optimizations such as runtime compilation of visualization specifications, parallelized execution, and hardware-accelerated rendering. We present benchmark studies measuring the performance gains provided by these optimizations and compare performance to existing Java-based visualization tools, demonstrating scalability improvements exceeding an order of magnitude. Bostock, M. Heer, J. hardware toolkit InfoVis IEEE Transactions on Visualization and Computer Graphics declarative languages domain specific languages information visualization optimization toolkit user interface 2010 infovis10--222 10/27/2010 IEEE Transactions on Visualization and Computer Graphics Visualizations Everywhere: A Multiplatform Infrastructure for Linked Visualizations. In order to use new visualizations, most toolkits require application developers to rebuild their applications and distribute new versions to users. The WebCharts Framework take a different approach by hosting Javascript from within an application and providing a standard data and events interchange.. In this way, applications can be extended dynamically, with a wide variety of visualizations. We discuss the benefits of this architectural approach, contrast it to existing techniques, and give a variety of examples and extensions of the basic system. Drucker, S.M. Fernandez, R. Fisher, D. Ruble, S. InfoVis IEEE Transactions on Visualization and Computer Graphics data transformation and representation toolkit design visualization systems 2010 infovis10--126 10/27/2010 IEEE Transactions on Visualization and Computer Graphics behaviorism: A Framework for Dynamic Data Visualization. While a number of information visualization software frameworks exist, creating new visualizations, especially those that involve novel visualization metaphors, interaction techniques, data analysis strategies, and specialized rendering algorithms, is still often a difficult process. To facilitate the creation of novel visualizations we present a new software framework, behaviorism, which provides a wide range of flexibility when working with dynamic information on visual, temporal, and ontological levels, but at the same time providing appropriate abstractions which allow developers to create prototypes quickly which can then easily be turned into robust systems. The core of the framework is a set of three interconnected graphs, each with associated operators: a scene graph for high-performance 3D rendering, a data graph for different layers of semantically linked heterogeneous data, and a timing graph for sophisticated control of scheduling, interaction, and animation. In particular, the timing graph provides a unified system to add behaviors to both data and visual elements, as well as to the behaviors themselves. To evaluate the framework we look briefly at three different projects all of which required novel visualizations in different domains, and all of which worked with dynamic data in different ways: an interactive ecological simulation, an information art installation, and an information visualization technique. Forbes, A.G. Hollerer, T. Legrady, G. animation graph interaction InfoVis IEEE Transactions on Visualization and Computer Graphics animation streaming data time-varying data visual design visualization system and toolkit design 2010 infovis10--154 10/27/2010 IEEE Transactions on Visualization and Computer Graphics FacetAtlas: Multifaceted Visualization for Rich Text Corpora. Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis. Cao, N. Gotz, D. Lin, Y. Liu, S. Qu, H. Sun, J. case study document education evaluation text InfoVis IEEE Transactions on Visualization and Computer Graphics multi-relational graph multifaceted visualization search ui text visualization 2010 infovis10--194 10/27/2010 IEEE Transactions on Visualization and Computer Graphics SparkClouds: Visualizing Trends in Tag Clouds. Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations. Carpendale, S. Henry Riche, N. Karlson, A.K. Lee, B. InfoVis IEEE Transactions on Visualization and Computer Graphics evaluation multiple line graphs stacked bar charts tag cloud trend visualization 2010 infovis10--175 10/27/2010 IEEE Transactions on Visualization and Computer Graphics ManiWordle: Providing Flexible Control over Wordle. Among the multifarious tag-clouding techniques, Wordle stands out to the community by providing an aesthetic layout, eliciting the emergence of the participatory culture and usage of tag-clouding in the artistic creations. In this paper, we introduce ManiWordle, a Wordle-based visualization tool that revamps interactions with the layout by supporting custom manipulations. ManiWordle allows people to manipulate typography, color, and composition not only for the layout as a whole, but also for the individual words, enabling them to have better control over the layout result. We first describe our design rationale along with the interaction techniques for tweaking the layout. We then present the results both from the preliminary usability study and from the comparative study between ManiWordle and Wordle. The results suggest that ManiWordle provides higher user satisfaction and an efficient method of creating the desired "art work," harnessing the power behind the ever-increasing popularity of Wordle. Kim, B. Koh, K. Lee, B. Seo, J. color interaction usability InfoVis IEEE Transactions on Visualization and Computer Graphics direct manipulation flexibilty-usability tradeoff interaction design participatory visualization tag cloud user study 2010 infovis11--188 10/26/2011 IEEE Transactions on Visualization and Computer Graphics DICON: Interactive Visual Analysis of Multidimensional Clusters. Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis. Cao, N. Gotz, D. Sun, J. Qu, H. case study cluster clustering evaluation interaction treemap user study InfoVis algorithm design and analysis clustering algorithms encoding image color analysis information analysis visualization IEEE Transactions on Visualization and Computer Graphics clustering information visualization visual analysis 2011 infovis11--202 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Flow Map Layout via Spiral Trees. Flow maps are thematic maps that visualize the movement of objects, such as people or goods, between geographic regions. One or more sources are connected to several targets by lines whose thickness corresponds to the amount of flow between a source and a target. Good flow maps reduce visual clutter by merging (bundling) lines smoothly and by avoiding self-intersections. Most flow maps are still drawn by hand and only few automated methods exist. Some of the known algorithms do not support edge- bundling and those that do, cannot guarantee crossing-free flows. We present a new algorithmic method that uses edge-bundling and computes crossing-free flows of high visual quality. Our method is based on so-called spiral trees, a novel type of Steiner tree which uses logarithmic spirals. Spiral trees naturally induce a clustering on the targets and smoothly bundle lines. Our flows can also avoid obstacles, such as map features, region outlines, or even the targets. We demonstrate our approach with extensive experiments. Buchin, K. Speckmann, B. Verbeek, K. clustering geographic InfoVis approximation algorithms cartography cost function steiner trees tree data structures IEEE Transactions on Visualization and Computer Graphics automated cartography flow maps spiral trees 2011 infovis11--255 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Visualization Rhetoric: Framing Effects in Narrative Visualization. Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels−the data, visual representation, textual annotations, and interactivity−and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation. Diakopoulos, N. Hullman, J. theory InfoVis data visualization rhetoric semiotics IEEE Transactions on Visualization and Computer Graphics connotation denotation framing effects narrative visualization rhetoric semiotics 2011 infovis11--253 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Visualization of Parameter Space for Image Analysis. Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step - initialization of sampling - and the last step - visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler - a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach. Bray, M. Carpenter, A.E. Pretorius, A.J. Ruddle, R.A. overview InfoVis algorithm design and analysis image analysis information processing sampling methods IEEE Transactions on Visualization and Computer Graphics image analysis information visualization parameter space sampling visual analytics 2011 infovis11--251 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Visual Thinking In Action: Visualizations As Used On Whiteboards. While it is still most common for information visualization researchers to develop new visualizations from a data- or taskdriven perspective, there is growing interest in understanding the types of visualizations people create by themselves for personal use. As part of this recent direction, we have studied a large collection of whiteboards in a research institution, where people make active use of combinations of words, diagrams and various types of visuals to help them further their thought processes. Our goal is to arrive at a better understanding of the nature of visuals that are created spontaneously during brainstorming, thinking, communicating, and general problem solving on whiteboards. We use the qualitative approaches of open coding, interviewing, and affinity diagramming to explore the use of recognizable and novel visuals, and the interplay between visualization and diagrammatic elements with words, numbers and labels. We discuss the potential implications of our findings on information visualization design. Carpendale, S. Fawcett, P. Henry Riche, N. Venolia, G. Walny, J. InfoVis data visualization encoding image color analysis IEEE Transactions on Visualization and Computer Graphics diagrams observational study visualization whiteboards 2011 infovis11--250 10/26/2011 IEEE Transactions on Visualization and Computer Graphics VisBricks: Multiform Visualization of Large, Inhomogeneous Data. Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner. In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents. State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine. Lex, A. Partl, C. Schmalstieg, D. Schulz, H. Streit, M. brushing cluster overview InfoVis data visualization nonhomogeneous media semantics IEEE Transactions on Visualization and Computer Graphics inhomogeneous data multiform visualization multiple coordinated views 2011 infovis11--247 10/26/2011 IEEE Transactions on Visualization and Computer Graphics TreeNetViz: Revealing Patterns of Networks over Tree Structures. Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. Gou, L. Zhang, X. case study graph interaction network radial social InfoVis algorithm design and analysis complexity theory data visualization graphics tree data structures IEEE Transactions on Visualization and Computer Graphics compound graph multiscale and cross-scale network and tree treenetviz visualization 2011 infovis11--239 10/26/2011 IEEE Transactions on Visualization and Computer Graphics TextFlow: Towards Better Understanding of Evolving Topics in Text. Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data. Cui, W. Gao, Z. Liu, S. Qu, H. Shi, C. Song, Y. Tan, L. Tong, X. interaction text InfoVis data visualization image color analysis tag clouds text analysis IEEE Transactions on Visualization and Computer Graphics critical event hierarchical dirichlet process text visualization topic evolution 2011 infovis11--237 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Synthetic Generation of High-Dimensional Datasets. Generation of synthetic datasets is a common practice in many research areas. Such data is often generated to meet specific needs or certain conditions that may not be easily found in the original, real data. The nature of the data varies according to the application area and includes text, graphs, social or weather data, among many others. The common process to create such synthetic datasets is to implement small scripts or programs, restricted to small problems or to a specific application. In this paper we propose a framework designed to generate high dimensional datasets. Users can interactively create and navigate through multi dimensional datasets using a suitable graphical user-interface. The data creation is driven by statistical distributions based on a few user-defined parameters. First, a grounding dataset is created according to given inputs, and then structures and trends are included in selected dimensions and orthogonal projection planes. Furthermore, our framework supports the creation of complex non-orthogonal trends and classified datasets. It can successfully be used to create synthetic datasets simulating important trends as multidimensional clusters, correlations and outliers. Albuquerque, G. Lowe, T. Magnor, M. social text InfoVis correlation data processing probability density function scattering parameters IEEE Transactions on Visualization and Computer Graphics high-dimensional data interaction multivariate data synthetic data generation 2011 infovis11--234 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Stereoscopic Highlighting: 2D Graph Visualization on Stereo Displays. In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases. Alper, B. Forbes, A.G. Hollerer, T. Kuchera-Morin, J. color experiment focus+context graph InfoVis data visualization graphics image color analysis stereo image processing two dimensional displays IEEE Transactions on Visualization and Computer Graphics graph visualization stereo displays virtual reality 2011 infovis11--233 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Skeleton-Based Edge Bundling for Graph Visualization. In this paper, we present a novel approach for constructing bundled layouts of general graphs. As layout cues for bundles, we use medial axes, or skeletons, of edges which are similar in terms of position information. We combine edge clustering, distance fields, and 2D skeletonization to construct progressively bundled layouts for general graphs by iteratively attracting edges towards the centerlines of level sets of their distance fields. Apart from clustering, our entire pipeline is image-based with an efficient implementation in graphics hardware. Besides speed and implementation simplicity, our method allows explicit control of the emphasis on structure of the bundled layout, i.e. the creation of strongly branching (organic-like) or smooth bundles. We demonstrate our method on several large real-world graphs. Cantareiro, G. Ersoy, O. Hurter, C. Paulovich, F.V. Telea, A.C. clustering graph hardware InfoVis image edge detection image processing shape analysis smoothing methods transforms IEEE Transactions on Visualization and Computer Graphics edge bundles graph layouts image-based information visualization 2011 infovis11--232 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Sequence Surveyor: Leveraging Overview for Scalable Genomic Alignment Visualization. In this paper, we introduce overview visualization tools for large-scale multiple genome alignment data. Genome alignment visualization and, more generally, sequence alignment visualization are an important tool for understanding genomic sequence data. As sequencing techniques improve and more data become available, greater demand is being placed on visualization tools to scale to the size of these new datasets. When viewing such large data, we necessarily cannot convey details, rather we specifically design overview tools to help elucidate large-scale patterns. Perceptual science, signal processing theory, and generality provide a framework for the design of such visualizations that can scale well beyond current approaches. We present Sequence Surveyor, a prototype that embodies these ideas for scalable multiple whole-genome alignment overview visualization. Sequence Surveyor visualizes sequences in parallel, displaying data using variable color, position, and aggregation encodings. We demonstrate how perceptual science can inform the design of visualization techniques that remain visually manageable at scale and how signal processing concepts can inform aggregation schemes that highlight global trends, outliers, and overall data distributions as the problem scales. These techniques allow us to visualize alignments with over 100 whole bacterial-sized genomes. Albers, D. Dewey, C. Gleicher, M. color overview theory InfoVis bioinformatics data visualization design methodology genomics image color analysis IEEE Transactions on Visualization and Computer Graphics bioinformatics visualization perception theory scalability issues visual design 2011 infovis11--229 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research. Bertini, E. Tatu, A. Keim, D.A. high-dimensional data metrics overview InfoVis data visualization measurements IEEE Transactions on Visualization and Computer Graphics high-dimensional data visualization quality metrics 2011 infovis11--227 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Product Plots. We propose a new framework for visualising tables of counts, proportions and probabilities. We call our framework product plots, alluding to the computation of area as a product of height and width, and the statistical concept of generating a joint distribution from the product of conditional and marginal distributions. The framework, with extensions, is sufficient to encompass over 20 visualisations previously described in fields of statistical graphics and infovis, including bar charts, mosaic plots, treemaps, equal area plots and fluctuation diagrams. Hofmann, H. Wickham, H. InfoVis data visualization image color analysis probability statistical analysis IEEE Transactions on Visualization and Computer Graphics bar chart conditional distribution joint distribution mosaic plot statistics treemap 2011 infovis11--226 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Parallel Edge Splatting for Scalable Dynamic Graph Visualization. We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-byside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words. Beck, F. Burch, M. Diehl, S. Vehlow, C. Weiskopf, D. brushing case study color graph history interaction pixel zooming InfoVis data visualization encoding graphics image color analysis image edge detection software engineering IEEE Transactions on Visualization and Computer Graphics dynamic graph visualization graph splatting software evolution software visualization 2011 infovis11--223 10/26/2011 IEEE Transactions on Visualization and Computer Graphics MoleView: An Attribute and Structure-Based Semantic Lens for Large Element-Based Plots. We present MoleView, a novel technique for interactive exploration of multivariate relational data. Given a spatial embedding of the data, in terms of a scatter plot or graph layout, we propose a semantic lens which selects a specific spatial and attribute-related data range. The lens keeps the selected data in focus unchanged and continuously deforms the data out of the selection range in order to maintain the context around the focus. Specific deformations include distance-based repulsion of scatter plot points, deforming straight-line node-link graph drawings, and as varying the simplification degree of bundled edge graph layouts. Using a brushing-based technique, we further show the applicability of our semantic lens for scenarios requiring a complex selection of the zones of interest. Our technique is simple to implement and provides real-time performance on large datasets. We demonstrate our technique with actual data from air and road traffic control, medical imaging, and software comprehension applications. Ersoy, O. Hurter, C. Telea, A.C. brushing graph graph layout InfoVis data visualization filtering theory lenses semantics shape analysis IEEE Transactions on Visualization and Computer Graphics attribute filtering graph bundling magic lenses semantic lenses 2011 infovis11--220 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Local Affine Multidimensional Projection. Multidimensional projection techniques have experienced many improvements lately, mainly regarding computational times and accuracy. However, existing methods do not yet provide flexible enough mechanisms for visualization-oriented fully interactive applications. This work presents a new multidimensional projection technique designed to be more flexible and versatile than other methods. This novel approach, called Local Affine Multidimensional Projection (LAMP), relies on orthogonal mapping theory to build accurate local transformations that can be dynamically modified according to user knowledge. The accuracy, flexibility and computational efficiency of LAMP is confirmed by a comprehensive set of comparisons. LAMP's versatility is exploited in an application which seeks to correlate data that, in principle, has no connection as well as in visual exploration of textual documents. Coimbra, D. Cuminato, J.A. Joia, P. Nonato, L.G. Paulovich, F.V. theory InfoVis data mining minimization robustness IEEE Transactions on Visualization and Computer Graphics high dimensional data multidimensional projection visual data mining 2011 infovis11--213 10/26/2011 IEEE Transactions on Visualization and Computer Graphics In Situ Exploration of Large Dynamic Networks. The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization– a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown – right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks. Hadlak, S. Schulz, H. Schumann, H. graph network social InfoVis data visualization graphics IEEE Transactions on Visualization and Computer Graphics dynamic graph data multi-focus+context multiform visualization 2011 infovis11--212 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Improved Similarity Trees and their Application to Visual Data Classification. An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification. Florian, L. Minghim, R. Paiva, J.G. Pedrini, H. Telles, G.P. data mining InfoVis algorithm design and analysis biomedical image processing data visualization image classification phylogeny IEEE Transactions on Visualization and Computer Graphics image classification multidimensional projection similarity trees 2011 infovis11--209 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Human-Centered Approaches in Geovisualization Design: Investigating Multiple Methods Through a Long-Term Case Study. Working with three domain specialists we investigate human-centered approaches to geovisualization following an ISO13407 taxonomy covering context of use, requirements and early stages of design. Our case study, undertaken over three years, draws attention to repeating trends: that generic approaches fail to elicit adequate requirements for geovis application design; that the use of real data is key to understanding needs and possibilities; that trust and knowledge must be built and developed with collaborators. These processes take time but modified human-centred approaches can be effective. A scenario developed through contextual inquiry but supplemented with domain data and graphics is useful to geovis designers. Wireframe, paper and digital prototypes enable successful communication between specialist and geovis domains when incorporating real and interesting data, prompting exploratory behaviour and eliciting previously unconsidered requirements. Paper prototypes are particularly successful at eliciting suggestions, especially for novel visualization. Enabling specialists to explore their data freely with a digital prototype is as effective as using a structured task protocol and is easier to administer. Autoethnography has potential for framing the design process. We conclude that a common understanding of context of use, domain data and visualization possibilities are essential to successful geovis design and develop as this progresses. HC approaches can make a significant contribution here. However, modified approaches, applied with flexibility, are most promising. We advise early, collaborative engagement with data – through simple, transient visual artefacts supported by data sketches and existing designs – before moving to successively more sophisticated data wireframes and data prototypes. Dykes, J. Lloyd, D. case study geovisualization taxonomy InfoVis data visualization domain specific languages human factors taxonomy IEEE Transactions on Visualization and Computer Graphics context of use design evaluation field study geovisualization prototypes requirements sketching 2011 infovis11--205 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Focus+Context Metro Maps. We introduce a focus+context method to visualize a complicated metro map of a modern city on a small displaying area. The context of our work is with regard the popularity of mobile devices. The best route to the destination, which can be obtained from the arrival time of trains, is highlighted. The stations on the route enjoy larger spaces, whereas the other stations are rendered smaller and closer to fit the whole map into a screen. To simplify the navigation and route planning for visitors, we formulate various map characteristics such as octilinear transportation lines and regular station distances into energy terms. We then solve for the optimal layout in a least squares sense. In addition, we label the names of stations that are on the route of a passenger according to human preferences, occlusions, and consistencies of label positions using the graph cuts method. Our system achieves real-time performance by being able to report instant information because of the carefully designed energy terms. We apply our method to layout a number of metro maps and show the results and timing statistics to demonstrate the feasibility of our technique. Chi, M. Wang, Y. focus+context graph navigation statistics InfoVis graphics layout nonlinear distortion optimization IEEE Transactions on Visualization and Computer Graphics focus+context visualization graph labeling metro map octilinear layout optimization 2011 infovis11--201 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Flexible Linked Axes for Multivariate Data Visualization. Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising. Claessen, J.H.T. van Wijk, J.J. brushing parallel coordinates InfoVis data visualization histograms image color analysis scattering parameters IEEE Transactions on Visualization and Computer Graphics multivariate data parallel coordinates plot scatterplot visualization 2011 infovis11--197 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Exploring Uncertainty in Geodemographics with Interactive Graphics. Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users' understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods. Dykes, J. Slingsby, A. Wood, J. uncertainty InfoVis classification data visualization demographics image color analysis visualization IEEE Transactions on Visualization and Computer Graphics cartography classification geodemographics oac uncertainty 2011 infovis11--196 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback. Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space. Bartram, L. Rodgers, J. aesthetics user study InfoVis art data visualization feedback real time systems resource management IEEE Transactions on Visualization and Computer Graphics ambient visualization casual infovis distributed visualization informative art sustainability 2011 infovis11--195 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Exploratory Analysis of Time-Series with ChronoLenses. Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines. Balakrishnan, R. Chevalier, F. Pietriga, E. Zhao, J. InfoVis data visualization lenses rendering (computer graphics) time series analysis transforms IEEE Transactions on Visualization and Computer Graphics exploratory visualization focus+context interaction techniques lens time-series data 2011 infovis11--193 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Evaluation of Traditional, Orthogonal, and Radial Tree Diagrams by an Eye Tracking Study. Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts. Burch, M. Heinrich, J. Hoeferlin, M. Konevtsova, N. Weiskopf, D. evaluation experiment hierarchy radial InfoVis analysis of variance data visualization hierarchical systems tracking trajectory upper bound IEEE Transactions on Visualization and Computer Graphics eye-tracking hierarchy visualization node-link layout user study 2011 infovis11--192 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Evaluation of Artery Visualizations for Heart Disease Diagnosis. Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map. Borkin, M. Feldman, C. Gajos, K. Melchionna, S. Mitsouras, D. Peters, A. Pfister, H. Rybicki, F. color evaluation taxonomy user study InfoVis arteries blood flow data visualization heart image color analysis three dimensional displays IEEE Transactions on Visualization and Computer Graphics biomedical and medical visualization qualitative evaluation quantitative evaluation 2011 infovis11--191 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Drawing Road Networks with Focus Regions. Mobile users of maps typically need detailed information about their surroundings plus some context information about remote places. In order to avoid that the map partly gets too dense, cartographers have designed mapping functions that enlarge a user-defined focus region - such functions are sometimes called fish-eye projections. The extra map space occupied by the enlarged focus region is compensated by distorting other parts of the map. We argue that, in a map showing a network of roads relevant to the user, distortion should preferably take place in those areas where the network is sparse. Therefore, we do not apply a predefined mapping function. Instead, we consider the road network as a graph whose edges are the road segments. We compute a new spatial mapping with a graph-based optimization approach, minimizing the square sum of distortions at edges. Our optimization method is based on a convex quadratic program (CQP); CQPs can be solved in polynomial time. Important requirements on the output map are expressed as linear inequalities. In particular, we show how to forbid edge crossings. We have implemented our method in a prototype tool. For instances of different sizes, our method generated output maps that were far less distorted than those generated with a predefined fish-eye projection. Future work is needed to automate the selection of roads relevant to the user. Furthermore, we aim at fast heuristics for application in real-time systems. Haunert, J. Sering, L. distortion graph network InfoVis cartography distortion measurement graphics image analysis optimization quadratic programming visualization IEEE Transactions on Visualization and Computer Graphics cartography fish-eye view graph drawing optimization quadratic programming schematic maps 2011 infovis11--190 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Divided Edge Bundling for Directional Network Data. The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs; we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns. Heer, J. Heller, B. Selassie, D. graph matrix network InfoVis data visualization encoding graphics image edge detection IEEE Transactions on Visualization and Computer Graphics aggregation edge bundling graph visualization node-link diagram physical simulation 2011 infovis11--187 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Developing and Evaluating Quilts for the Depiction of Large Layered Graphs. Traditional layered graph depictions such as flow charts are in wide use. Yet as graphs grow more complex, these depictions can become difficult to understand. Quilts are matrix-based depictions for layered graphs designed to address this problem. In this research, we first improve Quilts by developing three design alternatives, and then compare the best of these alternatives to better-known node-link and matrix depictions. A primary weakness in Quilts is their depiction of skip links, links that do not simply connect to a succeeding layer. Therefore in our first study, we compare Quilts using color-only, text-only, and mixed (color and text) skip link depictions, finding that path finding with the color-only depiction is significantly slower and less accurate, and that in certain cases, the mixed depiction offers an advantage over the text-only depiction. In our second study, we compare Quilts using the mixed depiction to node-link diagrams and centered matrices. Overall results show that users can find paths through graphs significantly faster with Quilts (46.6 secs) than with node-link (58.3 secs) or matrix (71.2 secs) diagrams. This speed advantage is still greater in large graphs (e.g. in 200 node graphs, 55.4 secs vs. 71.1 secs for node-link and 84.2 secs for matrix depictions). Bae, J. Watson, B. color graph matrix text InfoVis analysis of variance atmospheric measurements graphics particle measurements time measurement IEEE Transactions on Visualization and Computer Graphics graph drawing layered graphs matrix based depiction node-link diagram 2011 infovis11--186 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Design Study of LineSets, a Novel Set Visualization Technique. Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e.g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks. Alper, B. Czerwinski, M. Henry Riche, N. Ramos, G. design study experiment social InfoVis accuracy data visualization geometry shape analysis social network services IEEE Transactions on Visualization and Computer Graphics clustering faceted data visualization graph visualization set visualization 2011 infovis11--185 10/26/2011 IEEE Transactions on Visualization and Computer Graphics D3 Data-Driven Documents. Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations. Bostock, M. Heer, J. Ogievetsky, V. animation document evaluation interaction toolkit InfoVis cascading style sheets data visualization debugging image color analysis information analysis IEEE Transactions on Visualization and Computer Graphics 2D graphics information visualization toolkits user interfaces 2011 infovis11--183 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Context-Preserving Visual Links. Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information. Lex, A. Schmalstieg, D. Steinberger, M. Streit, M. Waldner, M. experiment occlusion InfoVis data visualization histograms image color analysis IEEE Transactions on Visualization and Computer Graphics connectedness highlighting image-based routing saliency visual links 2011 infovis11--181 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Composite Density Maps for Multivariate Trajectories. We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. [25] are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains. Andrienko, G. Andrienko, N. Scheepens, R. Willems, N. van Wijk, J.J. van de Wetering, H. InfoVis computational modeling computer architecture data visualization image color analysis trajectory IEEE Transactions on Visualization and Computer Graphics and raster maps geographical information systems kernel density estimation multivariate data trajectories 2011 infovis11--176 10/26/2011 IEEE Transactions on Visualization and Computer Graphics BirdVis: Visualizing and Understanding Bird Populations. Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain. Ferreira, N. Fink, D. Freire, J. Kelling, S. Lins, L. Silva, C.T. Wood, C. collaboration coordinated views InfoVis biological system modeling data visualization ornithology predictive models spatial databases tag clouds IEEE Transactions on Visualization and Computer Graphics multiscale analysis ornithology spatial data species distribution models temporal data 2011 infovis11--175 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Benefitting InfoVis with Visual Difficulties. Many well-cited theories for visualization design state that a visual representation should be optimized for quick and immediate interpretation by a user. Distracting elements like decorative "chartjunk" or extraneous information are avoided so as not to slow comprehension. Yet several recent studies in visualization research provide evidence that non-efficient visual elements may benefit comprehension and recall on the part of users. Similarly, findings from studies related to learning from visual displays in various subfields of psychology suggest that introducing cognitive difficulties to visualization interaction can improve a user's understanding of important information. In this paper, we synthesize empirical results from cross-disciplinary research on visual information representations, providing a counterpoint to efficiency-based design theory with guidelines that describe how visual difficulties can be introduced to benefit comprehension and recall. We identify conditions under which the application of visual difficulties is appropriate based on underlying factors in visualization interaction like active processing and engagement. We characterize effective graph design as a trade-off between efficiency and learning difficulties in order to provide Information Visualization (InfoVis) researchers and practitioners with a framework for organizing explorations of graphs for which comprehension and recall are crucial. We identify implications of this view for the design and evaluation of information visualizations. Adar, E. Hullman, J. Shah, P. evaluation graph interaction theory InfoVis cognition data visualization psychology time factors IEEE Transactions on Visualization and Computer Graphics active processing cognitive efficiency desirable difficulites engagement individual differences 2011 infovis11--174 10/26/2011 IEEE Transactions on Visualization and Computer Graphics BallotMaps: Detecting Name Bias in Alphabetically Ordered Ballot Papers. The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections. Badawood, D. Dykes, J. Slingsby, A. Wood, J. design study geographic InfoVis data visualization geospatial analysis image color analysis local government nominations and elections IEEE Transactions on Visualization and Computer Graphics bias democracy election geovisualization governance hierarchy treemap voting 2011 infovis11--169 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Asymmetric Relations in Longitudinal Social Networks. In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages. Brandes, U. Nick, B. matrix network small multiples social theory InfoVis data visualization image color analysis social network services IEEE Transactions on Visualization and Computer Graphics glyphbased techniques network visualization social networks time series data visual knowledge discovery and representation 2011 infovis11--167 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Arc Length-Based Aspect Ratio Selection. The aspect ratio of a plot has a dramatic impact on our ability to perceive trends and patterns in the data. Previous approaches for automatically selecting the aspect ratio have been based on adjusting the orientations or angles of the line segments in the plot. In contrast, we recommend a simple, effective method for selecting the aspect ratio: minimize the arc length of the data curve while keeping the area of the plot constant. The approach is parameterization invariant, robust to a wide range of inputs, preserves visual symmetries in the data, and is a compromise between previously proposed techniques. Further, we demonstrate that it can be effectively used to select the aspect ratio of contour plots. We believe arc length should become the default aspect ratio selection method. Talbot, J. Gerth, J. Hanrahan, P. InfoVis length measurement ratio selection time series analysis IEEE Transactions on Visualization and Computer Graphics aspect ratio selection banking to 45 degrees orientation resolution 2011 infovis11--166 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Angular Histograms: Frequency-Based Visualizations for Large, High Dimensional Data. Parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms. Geng, Z. Laramee, R.S. Peng, Z. Roberts, J.C. Walker, R. clustering high-dimensional data overview parallel coordinates InfoVis data visualization histograms vectors IEEE Transactions on Visualization and Computer Graphics angular histogram attribute curves parallel coordinates 2011 infovis11--163 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Adaptive Privacy-Preserving Visualization Using Parallel Coordinates. Current information visualization techniques assume unrestricted access to data. However, privacy protection is a key issue for a lot of real-world data analyses. Corporate data, medical records, etc. are rich in analytical value but cannot be shared without first going through a transformation step where explicit identifiers are removed and the data is sanitized. Researchers in the field of data mining have proposed different techniques over the years for privacy-preserving data publishing and subsequent mining techniques on such sanitized data. A well-known drawback in these methods is that for even a small guarantee of privacy, the utility of the datasets is greatly reduced. In this paper, we propose an adaptive technique for privacy preser vation in parallel coordinates. Based on knowledge about the sensitivity of the data, we compute a clustered representation on the fly, which allows the user to explore the data without breaching privacy. Through the use of screen-space privacy metrics, the technique adapts to the user's screen parameters and interaction. We demonstrate our method in a case study and discuss potential attack scenarios. Dasgupta, A. Kosara, R. case study data mining interaction metrics parallel coordinates InfoVis clustering algorithms data privacy data visualization privacy IEEE Transactions on Visualization and Computer Graphics clustering parallel coordinates privacy 2011 infovis11--160 10/26/2011 IEEE Transactions on Visualization and Computer Graphics A Study on Dual-Scale Data Charts. We present the results of a user study that compares different ways of representing Dual-Scale data charts. Dual-Scale charts incorporate two different data resolutions into one chart in order to emphasize data in regions of interest or to enable the comparison of data from distant regions. While some design guidelines exist for these types of charts, there is currently little empirical evidence on which to base their design. We fill this gap by discussing the design space of Dual-Scale cartesian-coordinate charts and by experimentally comparing the performance of different chart types with respect to elementary graphical perception tasks such as comparing lengths and distances. Our study suggests that cut-out charts which include collocated full context and focus are the best alternative, and that superimposed charts in which focus and context overlap on top of each other should be avoided. Bezerianos, A. Dragicevic, P. Fekete, J.-D. Isenberg, P. perception user study InfoVis data visualization image color analysis quantization shape analysis terminology IEEE Transactions on Visualization and Computer Graphics dual-scale charts focus+context quantitative experiment 2011 infovis11--257 10/26/2011 IEEE Transactions on Visualization and Computer Graphics Brushing dimensions - a dual visual analysis model for high-dimensional data. In many application fields, data analysts have to deal with datasets that contain many expressions per item. The effective analysis of such multivariate datasets is dependent on the user's ability to understand both the intrinsic dimensionality of the dataset as well as the distribution of the dependent values with respect to the dimensions. In this paper, we propose a visualization model that enables the joint interactive visual analysis of multivariate datasets with respect to their dimensions as well as with respect to the actual data values. We describe a dual setting of visualization and interaction in items space and in dimensions space. The visualization of items is linked to the visualization of dimensions with brushing and focus+context visualization. With this approach, the user is able to jointly study the structure of the dimensions space as well as the distribution of data items with respect to the dimensions. Even though the proposed visualization model is general, we demonstrate its application in the context of a DNA microarray data analysis. Filzmoser, P. Hauser, H. Turkay, C. brushing focus+context high-dimensional data interaction InfoVis analytical models computational modeling data models data visualization principal component analysis IEEE Transactions on Visualization and Computer Graphics high-dimensional data analysis interactive visual analysis 2011 infovis11--259 10/26/2011 IEEE Transactions on Visualization and Computer Graphics CloudLines: Compact Display of Event Episodes in Multiple Time-Series. We propose incremental logarithmic time-series technique as a way to deal with time-based representations of large and dynamic event data sets in limited space. Modern data visualization problems in the domains of news analysis, network security and financial applications, require visual analysis of incremental data, which poses specific challenges that are normally not solved by static visualizations. The incremental nature of the data implies that visualizations have to necessarily change their content and still provide comprehensible representations. In particular, in this paper we deal with the need to keep an eye on recent events together with providing a context on the past and to make relevant patterns accessible at any scale. Our technique adapts to the incoming data by taking care of the rate at which data items occur and by using a decay function to let the items fade away according to their relevance. Since access to details is also important, we also provide a novel distortion magnifying lens technique which takes into account the distortions introduced by the logarithmic time scale to augment readability in selected areas of interest. We demonstrate the validity of our techniques by applying them on incremental data coming from online news streams in different time frames. Bertini, E. Keim, D.A. Krstajic, M. distortion financial network security InfoVis data visualization estimation event detection lenses time series analysis IEEE Transactions on Visualization and Computer Graphics event based data incremental visualization lens distortion 2011 infovis12--189 10/16/2012 IEEE Transactions on Visualization and Computer Graphics A User Study on Curved Edges in Graph Visualization. Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges. Ham, D. Nguyen, P.H. Passmore, P. Rooney, C. Xu, K. aesthetics graph network user study InfoVis analysis of variance educational institutions layout optimization software user interfaces visualization IEEE Transactions on Visualization and Computer Graphics curved edges evaluation graph visualization 2012 infovis12--192 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Adaptive Composite Map Projections. All major web mapping services use the web Mercator projection. This is a poor choice for maps of the entire globe or areas of the size of continents or larger countries because the Mercator projection shows medium and higher latitudes with extreme areal distortion and provides an erroneous impression of distances and relative areas. The web Mercator projection is also not able to show the entire globe, as polar latitudes cannot be mapped. When selecting an alternative projection for information visualization, rivaling factors have to be taken into account, such as map scale, the geographic area shown, the map's height-to-width ratio, and the type of cartographic visualization. It is impossible for a single map projection to meet the requirements for all these factors. The proposed composite map projection combines several projections that are recommended in cartographic literature and seamlessly morphs map space as the user changes map scale or the geographic region displayed. The composite projection adapts the map's geometry to scale, to the map's height-to-width ratio, and to the central latitude of the displayed area by replacing projections and adjusting their parameters. The composite projection shows the entire globe including poles; it portrays continents or larger countries with less distortion (optionally without areal distortion); and it can morph to the web Mercator projection for maps showing small regions. Jenny, B. distortion geographic InfoVis continents decision trees earth interpolation mapping shape analysis IEEE Transactions on Visualization and Computer Graphics html5 canvas multi-scale map web cartography web map projection web mapping web mercator 2012 infovis12--193 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Algorithms for Labeling Focus Regions. In this paper, we investigate the problem of labeling point sites in focus regions of maps or diagrams. This problem occurs, for example, when the user of a mapping service wants to see the names of restaurants or other POIs in a crowded downtown area but keep the overview over a larger area. Our approach is to place the labels at the boundary of the focus region and connect each site with its label by a linear connection, which is called a leader. In this way, we move labels from the focus region to the less valuable context region surrounding it. In order to make the leader layout well readable, we present algorithms that rule out crossings between leaders and optimize other characteristics such as total leader length and distance between labels. This yields a new variant of the boundary labeling problem, which has been studied in the literature. Other than in traditional boundary labeling, where leaders are usually schematized polylines, we focus on leaders that are either straight-line segments or Bezier curves. Further, we present algorithms that, given the sites, find a position of the focus region that optimizes the above characteristics. We also consider a variant of the problem where we have more sites than space for labels. In this situation, we assume that the sites are prioritized by the user. Alternatively, we take a new facility-location perspective which yields a clustering of the sites. We label one representative of each cluster. If the user wishes, we apply our approach to the sites within a cluster, giving details on demand. Fink, M. Haunert, J. Schulz, A. Spoerhase, J. Wolff, A. cluster clustering overview InfoVis clustering methods data visualization geospatial analysis gravity labels ubiquitous computing visual analytics IEEE Transactions on Visualization and Computer Graphics data clustering focus+context techniques geographic/geospatial visualization mobile and ubiquitous visualization 2012 infovis12--196 10/16/2012 IEEE Transactions on Visualization and Computer Graphics An Empirical Model of Slope Ratio Comparisons. Comparing slopes is a fundamental graph reading task and the aspect ratio chosen for a plot influences how easy these comparisons are to make. According to Banking to 45¡, a classic design guideline first proposed and studied by Cleveland et al., aspect ratios that center slopes around 45¡ minimize errors in visual judgments of slope ratios. This paper revisits this earlier work. Through exploratory pilot studies that expand Cleveland et al.'s experimental design, we develop an empirical model of slope ratio estimation that fits more extreme slope ratio judgments and two common slope ratio estimation strategies. We then run two experiments to validate our model. In the first, we show that our model fits more generally than the one proposed by Cleveland et al. and we find that, in general, slope ratio errors are not minimized around 45¡. In the second experiment, we explore a novel hypothesis raised by our model: that visible baselines can substantially mitigate errors made in slope judgments. We conclude with an application of our model to aspect ratio selection. Gerth, J. Hanrahan, P. Talbot, J. experiment graph InfoVis approximation methods data models estimation market research predictive models slope analysis IEEE Transactions on Visualization and Computer Graphics aspect ratio selection banking to 45 degrees orientation resolution slope perception 2012 infovis12--197 10/16/2012 IEEE Transactions on Visualization and Computer Graphics An Empirical Study on Using Visual Embellishments in Visualization. In written and spoken communications, figures of speech (e.g., metaphors and synecdoche) are often used as an aid to help convey abstract or less tangible concepts. However, the benefits of using rhetorical illustrations or embellishments in visualization have so far been inconclusive. In this work, we report an empirical study to evaluate hypotheses that visual embellishments may aid memorization, visual search and concept comprehension. One major departure from related experiments in the literature is that we make use of a dual-task methodology in our experiment. This design offers an abstraction of typical situations where viewers do not have their full attention focused on visualization (e.g., in meetings and lectures). The secondary task introduces "divided attention", and makes the effects of visual embellishments more observable. In addition, it also serves as additional masking in memory-based trials. The results of this study show that visual embellishments can help participants better remember the information depicted in visualization. On the other hand, visual embellishments can have a negative impact on the speed of visual search. The results show a complex pattern as to the benefits of visual embellishments in helping participants grasp key concepts from visualization. Abdul-Rahman, A. Borgo, R. Chen, M. Floridi, L. Grant, P.W. Mohamed, F. Reppa, I. experiment InfoVis complexity theory data visualization grasping humans memory management speech visualization IEEE Transactions on Visualization and Computer Graphics cognition evaluation icons long-term memory metaphors visual embellishments visual search working memory 2012 infovis12--199 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing. People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populations with a specific background have been involved. In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. Visualizations included area-proportional Euler diagrams, glyph representations, and hybrid diagrams combining both. Our study failed to replicate previous findings in that subjects' accuracy was remarkably lower and visualizations exhibited no measurable benefit. A second experiment confirmed that simply adding a visualization to a textual Bayesian problem is of little help, even when the text refers to the visualization, but suggests that visualizations are more effective when the text is given without numerical values. We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts. Dragicevic, P. Fekete, J.-D. Micallef, L. experiment glyph text InfoVis bayesian methods breast cancer crowdsourcing sociology statistical analysis visualization IEEE Transactions on Visualization and Computer Graphics base rate fallacy bayesian reasoning crowdsourcing euler diagrams glyphs probabilistic judgment 2012 infovis12--204 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Beyond Mouse and Keyboard: Expanding Design Considerations for Information Visualization Interactions. The importance of interaction to Information Visualization (InfoVis) and, in particular, of the interplay between interactivity and cognition is widely recognized [12, 15, 32, 55, 70]. This interplay, combined with the demands from increasingly large and complex datasets, is driving the increased significance of interaction in InfoVis. In parallel, there have been rapid advances in many facets of interaction technologies. However, InfoVis interactions have yet to take full advantage of these new possibilities in interaction technologies, as they largely still employ the traditional desktop, mouse, and keyboard setup of WIMP (Windows, Icons, Menus, and a Pointer) interfaces. In this paper, we reflect more broadly about the role of more "natural" interactions for InfoVis and provide opportunities for future research. We discuss and relate general HCI interaction models to existing InfoVis interaction classifications by looking at interactions from a novel angle, taking into account the entire spectrum of interactions. Our discussion of InfoVis-specific interaction design considerations helps us identify a series of underexplored attributes of interaction that can lead to new, more "natural," interaction techniques for InfoVis. Carpendale, S. Henry Riche, N. Isenberg, P. Lee, B. cognition interaction InfoVis data visualization human computer interaction information analysis instruments taxonomy user interfaces IEEE Transactions on Visualization and Computer Graphics design considerations interaction nui (natural user interface) post-wimp 2012 infovis12--205 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Capturing the Design Space of Sequential Space-Filling Layouts. We characterize the design space of the algorithms that sequentially tile a rectangular area with smaller, fixed-surface, rectangles. This space consist of five independent dimensions: Order, Size, Score, Recurse and Phrase. Each of these dimensions describe a particular aspect of such layout tasks. This class of layouts is interesting, because, beyond encompassing simple grids, tables and trees, it also includes all kinds of treemaps involving the placement of rectangles. For instance, Slice and dice, Squarified, Strip and Pivot layouts are various points in this five dimensional space. Many classic statistics visualizations, such as 100% stacked bar charts, mosaic plots and dimensional stacking, are also instances of this class. A few new and potentially interesting points in this space are introduced, such as spiral treemaps and variations on the strip layout. The core algorithm is implemented as a JavaScript prototype that can be used as a layout component in a variety of InfoViz toolkits. Baudel, T. Broeskema, B. statistics InfoVis algorithm design and analysis layout spirals tree data structures IEEE Transactions on Visualization and Computer Graphics dimensional stacking grids layout mosaic plots squarified and pivot variations) strip tables & tree layouts treemaps (slice and dice visualization models 2012 infovis12--207 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Comparing Clusterings Using Bertin's Idea. Classifying a set of objects into clusters can be done in numerous ways, producing different results. They can be visually compared using contingency tables [27], mosaicplots [13], fluctuation diagrams [15], tableplots [20] , (modified) parallel coordinates plots [28], Parallel Sets plots [18] or circos diagrams [19]. Unfortunately the interpretability of all these graphical displays decreases rapidly with the numbers of categories and clusterings. In his famous book A Semiology of Graphics [5] Bertin writes "the discovery of an ordered concept appears as the ultimate point in logical simplification since it permits reducing to a single instant the assimilation of series which previously required many instants of study". Or in more everyday language, if you use good orderings you can see results immediately that with other orderings might take a lot of effort. This is also related to the idea of effect ordering [12], that data should be organised to reflect the effect you want to observe. This paper presents an efficient algorithm based on Bertin's idea and concepts related to Kendall's t [17], which finds informative joint orders for two or more nominal classification variables. We also show how these orderings improve the various displays and how groups of corresponding categories can be detected using a top-down partitioning algorithm. Different clusterings based on data on the environmental performance of cars sold in Germany are used for illustration. All presented methods are available in the R package extracat which is used to compute the optimized orderings for the example dataset. Gribov, A. Pilhofer, A. Unwin, A. nominal parallel coordinates InfoVis classification clustering algorithms graphics optimization stress measurement IEEE Transactions on Visualization and Computer Graphics classification fluctuation diagrams order optimization seriation 2012 infovis12--208 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Compressed Adjacency Matrices: Untangling Gene Regulatory Networks. We present a novel technique-Compressed Adjacency Matrices-for visualizing gene regulatory networks. These directed networks have strong structural characteristics: out-degrees with a scale-free distribution, in-degrees bound by a low maximum, and few and small cycles. Standard visualization techniques, such as node-link diagrams and adjacency matrices, are impeded by these network characteristics. The scale-free distribution of out-degrees causes a high number of intersecting edges in node-link diagrams. Adjacency matrices become space-inefficient due to the low in-degrees and the resulting sparse network. Compressed adjacency matrices, however, exploit these structural characteristics. By cutting open and rearranging an adjacency matrix, we achieve a compact and neatly-arranged visualization. Compressed adjacency matrices allow for easy detection of subnetworks with a specific structure, so-called motifs, which provide important knowledge about gene regulatory networks to domain experts. We summarize motifs commonly referred to in the literature, and relate them to network analysis tasks common to the visualization domain. We show that a user can easily find the important motifs in compressed adjacency matrices, and that this is hard in standard adjacency matrix and node-link diagrams. We also demonstrate that interaction techniques for standard adjacency matrices can be used for our compressed variant. These techniques include rearrangement clustering, highlighting, and filtering. Dinkla, K. Westenberg, M.A. van Wijk, J.J. clustering interaction matrix network InfoVis bismuth computer aided manufacturing layout proteins sparse matrices standards visualization IEEE Transactions on Visualization and Computer Graphics adjacency matrix gene regulation network scale-free 2012 infovis12--212 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Design Considerations for Optimizing Storyline Visualizations. Storyline visualization is a technique used to depict the temporal dynamics of social interactions. This visualization technique was first introduced as a hand-drawn illustration in XKCD's "Movie Narrative Charts" [21]. If properly constructed, the visualization can convey both global trends and local interactions in the data. However, previous methods for automating storyline visualizations are overly simple, failing to achieve some of the essential principles practiced by professional illustrators. This paper presents a set of design considerations for generating aesthetically pleasing and legible storyline visualizations. Our layout algorithm is based on evolutionary computation, allowing us to effectively incorporate multiple objective functions. We show that the resulting visualizations have significantly improved aesthetics and legibility compared to existing techniques. Ma, K.-L. Tanahashi, Y. aesthetics social InfoVis data visualization design methodology genomics layout motion pictures white spaces IEEE Transactions on Visualization and Computer Graphics design study layout algorithm storyline visualization timeline visualization 2012 infovis12--213 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Design Study Methodology: Reflections from the Trenches and the Stacks. Design studies are an increasingly popular form of problem-driven visualization research, yet there is little guidance available about how to do them effectively. In this paper we reflect on our combined experience of conducting twenty-one design studies, as well as reading and reviewing many more, and on an extensive literature review of other field work methods and methodologies. Based on this foundation we provide definitions, propose a methodological framework, and provide practical guidance for conducting design studies. We define a design study as a project in which visualization researchers analyze a specific real-world problem faced by domain experts, design a visualization system that supports solving this problem, validate the design, and reflect about lessons learned in order to refine visualization design guidelines. We characterize two axes - a task clarity axis from fuzzy to crisp and an information location axis from the domain expert's head to the computer - and use these axes to reason about design study contributions, their suitability, and uniqueness from other approaches. The proposed methodological framework consists of 9 stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. For each stage we provide practical guidance and outline potential pitfalls. We also conducted an extensive literature survey of related methodological approaches that involve a significant amount of qualitative field work, and compare design study methodology to that of ethnography, grounded theory, and action research. Meyer, M. Munzner, T. Sedlmair, M. design study theory InfoVis algorithm design and analysis collaboration data visualization design methodology logic gates visualization IEEE Transactions on Visualization and Computer Graphics design study framework methodology visualization 2012 infovis12--214 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Different Strokes for Different Folks: Visual Presentation Design between Disciplines. We present an ethnographic study of design differences in visual presentations between academic disciplines. Characterizing design conventions between users and data domains is an important step in developing hypotheses, tools, and design guidelines for information visualization. In this paper, disciplines are compared at a coarse scale between four groups of fields: social, natural, and formal sciences; and the humanities. Two commonplace presentation types were analyzed: electronic slideshows and whiteboard "chalk talks". We found design differences in slideshows using two methods - coding and comparing manually-selected features, like charts and diagrams, and an image-based analysis using PCA called eigenslides. In whiteboard talks with controlled topics, we observed design behaviors, including using representations and formalisms from a participant's own discipline, that suggest authors might benefit from novel assistive tools for designing presentations. Based on these findings, we discuss opportunities for visualization ethnography and human-centered authoring tools for visual information. Gomez, S.R. Guo, H. Jianu, R. Laidlaw, D.H. Ziemkiewicz, C. social InfoVis buildings cognitive science educational institutions encoding principal component analysis semantics visualization IEEE Transactions on Visualization and Computer Graphics design information visualization presentations visual analysis 2012 infovis12--215 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Does an Eye Tracker Tell the Truth about Visualizations?: Findings while Investigating Visualizations for Decision Making. For information visualization researchers, eye tracking has been a useful tool to investigate research participants' underlying cognitive processes by tracking their eye movements while they interact with visual techniques. We used an eye tracker to better understand why participants with a variant of a tabular visualization called `SimulSort' outperformed ones with a conventional table and typical one-column sorting feature (i.e., Typical Sorting). The collected eye-tracking data certainly shed light on the detailed cognitive processes of the participants; SimulSort helped with decision-making tasks by promoting efficient browsing behavior and compensatory decision-making strategies. However, more interestingly, we also found unexpected eye-tracking patterns with Simul- Sort. We investigated the cause of the unexpected patterns through a crowdsourcing-based study (i.e., Experiment 2), which elicited an important limitation of the eye tracking method: incapability of capturing peripheral vision. This particular result would be a caveat for other visualization researchers who plan to use an eye tracker in their studies. In addition, the method to use a testing stimulus (i.e., influential column) in Experiment 2 to verify the existence of such limitations would be useful for researchers who would like to verify their eye tracking results. Dong, Z. Kim, S.-H. Upatising, B. Xian, H. Yi, J.S. experiment InfoVis crowdsourcing data visualization decision making market research research and development tracking IEEE Transactions on Visualization and Computer Graphics crowdsourcing eye tracking limitations peripheral vision quantitative empirical study visualized decision making 2012 infovis12--220 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty. We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks. Bezerianos, A. Boukhelifa, N. Fekete, J.-D. Isenberg, T. evaluation perception uncertainty InfoVis data visualization gray-scale image color analysis rendering (computer graphics) shape analysis uncertainty IEEE Transactions on Visualization and Computer Graphics perception qualitative evaluation quantitative evaluation uncertainty visualization 2012 infovis12--221 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Evaluating the Effect of Style in Information Visualization. This paper reports on a between-subject, comparative online study of three information visualization demonstrators that each displayed the same dataset by way of an identical scatterplot technique, yet were different in style in terms of visual and interactive embellishment. We validated stylistic adherence and integrity through a separate experiment in which a small cohort of participants assigned our three demonstrators to predefined groups of stylistic examples, after which they described the styles with their own words. From the online study, we discovered significant differences in how participants execute specific interaction operations, and the types of insights that followed from them. However, in spite of significant differences in apparent usability, enjoyability and usefulness between the style demonstrators, no variation was found on the self-reported depth, expert-rated depth, confidence or difficulty of the resulting insights. Three different methods of insight analysis have been applied, revealing how style impacts the creation of insights, ranging from higher-level pattern seeking to a more reflective and interpretative engagement with content, which is what underlies the patterns. As this study only forms the first step in determining how the impact of style in information visualization could be best evaluated, we propose several guidelines and tips on how to gather, compare and categorize insights through an online evaluation study, particularly in terms of analyzing the concise, yet wide variety of insights and observations in a trustworthy and reproducable manner. Christoph, B. Grechenig, T. Tomitsch, M. Vande Moere, A. Wimmer, C. evaluation experiment insight interaction scatterplot usability InfoVis abstracts benchmark testing data visualization electronic mail subspace constraints usability visualization IEEE Transactions on Visualization and Computer Graphics aesthetics design evaluation online study style user experience visualization 2012 infovis12--225 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization. Event sequence data is common in many domains, ranging from electronic medical records (EMRs) to sports events. Moreover, such sequences often result in measurable outcomes (e.g., life or death, win or loss). Collections of event sequences can be aggregated together to form event progression pathways. These pathways can then be connected with outcomes to model how alternative chains of events may lead to different results. This paper describes the Outflow visualization technique, designed to (1) aggregate multiple event sequences, (2) display the aggregate pathways through different event states with timing and cardinality, (3) summarize the pathways' corresponding outcomes, and (4) allow users to explore external factors that correlate with specific pathway state transitions. Results from a user study with twelve participants show that users were able to learn how to use Outflow easily with limited training and perform a range of tasks both accurately and rapidly. Gotz, D. Wongsuphasawat, K. user study InfoVis data visualization image color analysis information analysis layout sequential analysis IEEE Transactions on Visualization and Computer Graphics information visualization outflow state diagram state transition temporal event sequences 2012 infovis12--226 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Facilitating Discourse Analysis with Interactive Visualization. A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree-one of the key data structures enabling applications such as text summarization, question answering and dialogue generation. Computational linguistics researchers currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. In this paper, we present DAViewer, an interactive visualization system for assisting computational linguistics researchers to explore, compare, evaluate and annotate the results of discourse parsers. An iterative user-centered design process with domain experts was conducted in the development of DAViewer. We report the results of an informal formative study of the system to better understand how the proposed visualization and interaction techniques are used in the real research environment. Balakrishnan, R. Chevalier, F. Collins, C. Zhao, J. document interaction text InfoVis algorithm design and analysis computational linguistics data visualization image color analysis prototypes standards visualization IEEE Transactions on Visualization and Computer Graphics computational linguisitics discourse structure interaction techniques tree comparison visual analytics 2012 infovis12--229 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Graphical Overlays: Using Layered Elements to Aid Chart Reading. Reading a visualization can involve a number of tasks such as extracting, comparing or aggregating numerical values. Yet, most of the charts that are published in newspapers, reports, books, and on the Web only support a subset of these tasks. In this paper we introduce graphical overlays-visual elements that are layered onto charts to facilitate a larger set of chart reading tasks. These overlays directly support the lower-level perceptual and cognitive processes that viewers must perform to read a chart. We identify five main types of overlays that support these processes; the overlays can provide (1) reference structures such as gridlines, (2) highlights such as outlines around important marks, (3) redundant encodings such as numerical data labels, (4) summary statistics such as the mean or max and (5) annotations such as descriptive text for context. We then present an automated system that applies user-chosen graphical overlays to existing chart bitmaps. Our approach is based on the insight that generating most of these graphical overlays only requires knowing the properties of the visual marks and axes that encode the data, but does not require access to the underlying data values. Thus, our system analyzes the chart bitmap to extract only the properties necessary to generate the desired overlay. We also discuss techniques for generating interactive overlays that provide additional controls to viewers. We demonstrate several examples of each overlay type for bar, pie and line charts. Agrawala, M. Kong, N. insight statistics text InfoVis bars data mining data visualization encoding image color analysis market research visualization IEEE Transactions on Visualization and Computer Graphics graph comprehension graphical perception overlays visualization 2012 infovis12--230 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Graphical Tests for Power Comparison of Competing Designs. Lineups [4, 28] have been established as tools for visual testing similar to standard statistical inference tests, allowing us to evaluate the validity of graphical findings in an objective manner. In simulation studies [12] lineups have been shown as being efficient: the power of visual tests is comparable to classical tests while being much less stringent in terms of distributional assumptions made. This makes lineups versatile, yet powerful, tools in situations where conditions for regular statistical tests are not or cannot be met. In this paper we introduce lineups as a tool for evaluating the power of competing graphical designs. We highlight some of the theoretical properties and then show results from two studies evaluating competing designs: both studies are designed to go to the limits of our perceptual abilities to highlight differences between designs. We use both accuracy and speed of evaluation as measures of a successful design. The first study compares the choice of coordinate system: polar versus cartesian coordinates. The results show strong support in favor of cartesian coordinates in finding fast and accurate answers to spotting patterns. The second study is aimed at finding shift differences between distributions. Both studies are motivated by data problems that we have recently encountered, and explore using simulated data to evaluate the plot designs under controlled conditions. Amazon Mechanical Turk (MTurk) is used to conduct the studies. The lineups provide an effective mechanism for objectively evaluating plot designs. Cook, D. Follett, L. Hofmann, H. Majumder, M. evaluation InfoVis accuracy data models inference mechanisms observers statistical analysis visual analytics IEEE Transactions on Visualization and Computer Graphics efficiency of displays lineups power comparison visual inference 2012 infovis12--233 10/16/2012 IEEE Transactions on Visualization and Computer Graphics How Capacity Limits of Attention Influence Information Visualization Effectiveness. In this paper, we explore how the capacity limits of attention influence the effectiveness of information visualizations. We conducted a series of experiments to test how visual feature type (color vs. motion), layout, and variety of visual elements impacted user performance. The experiments tested users' abilities to (1) determine if a specified target is on the screen, (2) detect an odd-ball, deviant target, different from the other visible objects, and (3) gain a qualitative overview by judging the number of unique categories on the screen. Our results show that the severe capacity limits of attention strongly modulate the effectiveness of information visualizations, particularly the ability to detect unexpected information. Keeping in mind these capacity limits, we conclude with a set of design guidelines which depend on a visualization's intended use. Haroz, S. Whitney, D. color overview InfoVis accuracy color data visualization image color analysis layout time factors visualization IEEE Transactions on Visualization and Computer Graphics attention color goal-oriented design layout motion nominal axis perception user study 2012 infovis12--236 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Intelligent Graph Layout Using Many Users' Input. In this paper, we propose a new strategy for graph drawing utilizing layouts of many sub-graphs supplied by a large group of people in a crowd sourcing manner. We developed an algorithm based on Laplacian constrained distance embedding to merge subgraphs submitted by different users, while attempting to maintain the topological information of the individual input layouts. To facilitate collection of layouts from many people, a light-weight interactive system has been designed to enable convenient dynamic viewing, modification and traversing between layouts. Compared with other existing graph layout algorithms, our approach can achieve more aesthetic and meaningful layouts with high user preference. Che, L. Hu, Y. Yuan, X. Zhang, X. graph graph drawing graph layout InfoVis algorithm design and analysis crowdsourcing human factors laplace equations layout stress IEEE Transactions on Visualization and Computer Graphics crowd sourcing editing force directed layout graph layout laplacian matrix merging stress model 2012 infovis12--237 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Interaction Support for Visual Comparison Inspired by Natural Behavior. Visual comparison is an intrinsic part of interactive data exploration and analysis. The literature provides a large body of existing solutions that help users accomplish comparison tasks. These solutions are mostly of visual nature and custom-made for specific data. We ask the question if a more general support is possible by focusing on the interaction aspect of comparison tasks. As an answer to this question, we propose a novel interaction concept that is inspired by real-world behavior of people comparing information printed on paper. In line with real-world interaction, our approach supports users (1) in interactively specifying pieces of graphical information to be compared, (2) in flexibly arranging these pieces on the screen, and (3) in performing the actual comparison of side-by-side and overlapping arrangements of the graphical information. Complementary visual cues and add-ons further assist users in carrying out comparison tasks. Our concept and the integrated interaction techniques are generally applicable and can be coupled with different visualization techniques. We implemented an interactive prototype and conducted a qualitative user study to assess the concept's usefulness in the context of three different visualization techniques. The obtained feedback indicates that our interaction techniques mimic the natural behavior quite well, can be learned quickly, and are easy to apply to visual comparison tasks. Forsell, C. Johansson, J. Tominski, C. interaction user study InfoVis animation computers data visualization layout navigation shape visualization IEEE Transactions on Visualization and Computer Graphics human-computer interaction interaction techniques natural interaction visual comparison visualization 2012 infovis12--238 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Interactive Level-of-Detail Rendering of Large Graphs. We propose a technique that allows straight-line graph drawings to be rendered interactively with adjustable level of detail. The approach consists of a novel combination of edge cumulation with density-based node aggregation and is designed to exploit common graphics hardware for speed. It operates directly on graph data and does not require precomputed hierarchies or meshes. As proof of concept, we present an implementation that scales to graphs with millions of nodes and edges, and discuss several example applications. Brandes, U. Deussen, O. Strobelt, H. Zinsmaier, M. graph hardware hierarchies InfoVis aggregates data visualization image color analysis image edge detection rendering (computer graphics) IEEE Transactions on Visualization and Computer Graphics edge aggregation graph visualization opengl 2012 infovis12--244 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Living Liquid: Design and Evaluation of an Exploratory Visualization Tool for Museum Visitors. Interactive visualizations can allow science museum visitors to explore new worlds by seeing and interacting with scientific data. However, designing interactive visualizations for informal learning environments, such as museums, presents several challenges. First, visualizations must engage visitors on a personal level. Second, visitors often lack the background to interpret visualizations of scientific data. Third, visitors have very limited time at individual exhibits in museums. This paper examines these design considerations through the iterative development and evaluation of an interactive exhibit as a visualization tool that gives museumgoers access to scientific data generated and used by researchers. The exhibit prototype, Living Liquid, encourages visitors to ask and answer their own questions while exploring the time-varying global distribution of simulated marine microbes using a touchscreen interface. Iterative development proceeded through three rounds of formative evaluations using think-aloud protocols and interviews, each round informing a key visualization design decision: (1) what to visualize to initiate inquiry, (2) how to link data at the microscopic scale to global patterns, and (3) how to include additional data that allows visitors to pursue their own questions. Data from visitor evaluations suggests that, when designing visualizations for public audiences, one should (1) avoid distracting visitors from data that they should explore, (2) incorporate background information into the visualization, (3) favor understandability over scientific accuracy, and (4) layer data accessibility to structure inquiry. Lessons learned from this case study add to our growing understanding of how to use visualizations to actively engage learners with scientific data. Frazier, J. Liao, I. Ma, J. Ma, K.-L. case study evaluation InfoVis data visualization image color analysis information analysis learning systems motion pictures performance evaluation prototypes IEEE Transactions on Visualization and Computer Graphics evaluation informal learning environments information visualization science museums user interaction user studies 2012 infovis12--245 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Memorability of Visual Features in Network Diagrams. We investigate the cognitive impact of various layout features-symmetry, alignment, collinearity, axis alignment and orthogonality - on the recall of network diagrams (graphs). This provides insight into how people internalize these diagrams and what features should or shouldn't be utilised when designing static and interactive network-based visualisations. Participants were asked to study, remember, and draw a series of small network diagrams, each drawn to emphasise a particular visual feature. The visual features were based on existing theories of perception, and the task enabled visual processing at the visceral level only. Our results strongly support the importance of visual features such as symmetry, collinearity and orthogonality, while not showing any significant impact for node-alignment or parallel edges. Goncu, C. Marriott, K. Purchase, H. Wybrow, M. insight network perception InfoVis algorithm design and analysis educational institutions image edge detection layout shape topology visualization IEEE Transactions on Visualization and Computer Graphics diagram recall experiment graph layout network diagrams perceptual theories visual features 2012 infovis12--250 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Organizing Search Results with a Reference Map. We propose a method to highlight query hits in hierarchically clustered collections of interrelated items such as digital libraries or knowledge bases. The method is based on the idea that organizing search results similarly to their arrangement on a fixed reference map facilitates orientation and assessment by preserving a user's mental map. Here, the reference map is built from an MDS layout of the items in a Voronoi treemap representing their hierarchical clustering, and we use techniques from dynamic graph layout to align query results with the map. The approach is illustrated on an archive of newspaper articles. Brandes, U. Nocaj, A. clustering graph graph layout treemap InfoVis edge detection query processing search methods tree data structures IEEE Transactions on Visualization and Computer Graphics dynamic graph layout edge bundling mental map multidimensional scaling search results voronoi treemaps 2012 infovis12--251 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Perception of Visual Variables on Tiled Wall-Sized Displays for Information Visualization Applications. We present the results of two user studies on the perception of visual variables on tiled high-resolution wall-sized displays. We contribute an understanding of, and indicators predicting how, large variations in viewing distances and viewing angles affect the accurate perception of angles, areas, and lengths. Our work, thus, helps visualization researchers with design considerations on how to create effective visualizations for these spaces. The first study showed that perception accuracy was impacted most when viewers were close to the wall but differently for each variable (Angle, Area, Length). Our second study examined the effect of perception when participants could move freely compared to when they had a static viewpoint. We found that a far but static viewpoint was as accurate but less time consuming than one that included free motion. Based on our findings, we recommend encouraging viewers to stand further back from the display when conducting perception estimation tasks. If tasks need to be conducted close to the wall display, important information should be placed directly in front of the viewer or above, and viewers should be provided with an estimation of the distortion effects predicted by our work-or encouraged to physically navigate the wall in specific ways to reduce judgement error. Bezerianos, A. Isenberg, P. distortion perception InfoVis data visualization information analysis navigation visual analytics IEEE Transactions on Visualization and Computer Graphics information visualization perception wall-displays 2012 infovis12--252 10/16/2012 IEEE Transactions on Visualization and Computer Graphics PivotPaths: Strolling through Faceted Information Spaces. We present PivotPaths, an interactive visualization for exploring faceted information resources. During both work and leisure, we increasingly interact with information spaces that contain multiple facets and relations, such as authors, keywords, and citations of academic publications, or actors and genres of movies. To navigate these interlinked resources today, one typically selects items from facet lists resulting in abrupt changes from one subset of data to another. While filtering is useful to retrieve results matching specific criteria, it can be difficult to see how facets and items relate and to comprehend the effect of filter operations. In contrast, the PivotPaths interface exposes faceted relations as visual paths in arrangements that invite the viewer to `take a stroll' through an information space. PivotPaths supports pivot operations as lightweight interaction techniques that trigger gradual transitions between views. We designed the interface to allow for casual traversal of large collections in an aesthetically pleasing manner that encourages exploration and serendipitous discoveries. This paper shares the findings from our iterative design-and-evaluation process that included semi-structured interviews and a two-week deployment of PivotPaths applied to a large database of academic publications. Dork, M. Dumais, S. Henry Riche, N. Ramos, G. database evaluation filter interaction InfoVis context facial animation information services layout motion pictures navigation visualization IEEE Transactions on Visualization and Computer Graphics animation exploratory search information seeking information visualization interactivity node-link diagrams 2012 infovis12--253 10/16/2012 IEEE Transactions on Visualization and Computer Graphics RankExplorer: Visualization of Ranking Changes in Large Time Series Data. For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations. Chen, W. Cui, W. Liu, S. Qu, H. Shi, C. Xu, P. color statistics time series InfoVis data visualization encoding image color analysis market research time series analysis IEEE Transactions on Visualization and Computer Graphics interaction techniques ranking change themeriver time-series data 2012 infovis12--255 10/16/2012 IEEE Transactions on Visualization and Computer Graphics RelEx: Visualization for Actively Changing Overlay Network Specifications. We present a network visualization design study focused on supporting automotive engineers who need to specify and optimize traffic patterns for in-car communication networks. The task and data abstractions that we derived support actively making changes to an overlay network, where logical communication specifications must be mapped to an underlying physical network. These abstractions are very different from the dominant use case in visual network analysis, namely identifying clusters and central nodes, that stems from the domain of social network analysis. Our visualization tool RelEx was created and iteratively refined through a full user-centered design process that included a full problem characterization phase before tool design began, paper prototyping, iterative refinement in close collaboration with expert users for formative evaluation, deployment in the field with real analysts using their own data, usability testing with non-expert users, and summative evaluation at the end of the deployment. In the summative post-deployment study, which entailed domain experts using the tool over several weeks in their daily practice, we documented many examples where the use of RelEx simplified or sped up their work compared to previous practices. Butz, A. Frank, A. Munzner, T. Sedlmair, M. collaboration design study evaluation network social usability InfoVis automotive engineering change detection algorithms collaboration data visualization network topology traffic control IEEE Transactions on Visualization and Computer Graphics automotive change management design study network visualization traffic optimization traffic routing 2012 infovis12--256 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data. Datasets with a large number of dimensions per data item (hundreds or more) are challenging both for computational and visual analysis. Moreover, these dimensions have different characteristics and relations that result in sub-groups and/or hierarchies over the set of dimensions. Such structures lead to heterogeneity within the dimensions. Although the consideration of these structures is crucial for the analysis, most of the available analysis methods discard the heterogeneous relations among the dimensions. In this paper, we introduce the construction and utilization of representative factors for the interactive visual analysis of structures in high-dimensional datasets. First, we present a selection of methods to investigate the sub-groups in the dimension set and associate representative factors with those groups of dimensions. Second, we introduce how these factors are included in the interactive visual analysis cycle together with the original dimensions. We then provide the steps of an analytical procedure that iteratively analyzes the datasets through the use of representative factors. We discuss how our methods improve the reliability and interpretability of the analysis process by enabling more informed selections of computational tools. Finally, we demonstrate our techniques on the analysis of brain imaging study results that are performed over a large group of subjects. Hauser, H. Lundervold, A. Lundervold, A.J. Turkay, C. hierarchies high-dimensional data InfoVis correlation data mining data visualization gaussian distribution principal component analysis reliability IEEE Transactions on Visualization and Computer Graphics high-dimensional data analysis interactive visual analysis 2012 infovis12--262 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Sketchy Rendering for Information Visualization. We present and evaluate a framework for constructing sketchy style information visualizations that mimic data graphics drawn by hand. We provide an alternative renderer for the Processing graphics environment that redefines core drawing primitives including line, polygon and ellipse rendering. These primitives allow higher-level graphical features such as bar charts, line charts, treemaps and node-link diagrams to be drawn in a sketchy style with a specified degree of sketchiness. The framework is designed to be easily integrated into existing visualization implementations with minimal programming modification or design effort. We show examples of use for statistical graphics, conveying spatial imprecision and for enhancing aesthetic and narrative qualities of visualization. We evaluate user perception of sketchiness of areal features through a series of stimulus-response tests in order to assess users' ability to place sketchiness on a ratio scale, and to estimate area. Results suggest relative area judgment is compromised by sketchy rendering and that its influence is dependent on the shape being rendered. They show that degree of sketchiness may be judged on an ordinal scale but that its judgement varies strongly between individuals. We evaluate higher-level impacts of sketchiness through user testing of scenarios that encourage user engagement with data visualization and willingness to critique visualization design. Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive. The results of our work have implications for effective information visualization design that go beyond the traditional role of sketching as a tool for prototyping or its use for an indication of general uncertainty. Boukhelifa, N. Dykes, J. Isenberg, P. Isenberg, T. Slingsby, A. Wood, J. ordinal perception uncertainty InfoVis data visualization rendering (computer graphics) shape analysis IEEE Transactions on Visualization and Computer Graphics hand-drawn non-photorealistic rendering npr sketch uncertainty visualization 2012 infovis12--263 10/16/2012 IEEE Transactions on Visualization and Computer Graphics SnapShot: Visualization to Propel Ice Hockey Analytics. Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel. Boyle, J.M. Pileggi, H. Stasko, J. Stolper, C.D. radial user study InfoVis data visualization games human computer interaction knowledge discovery sports equipment IEEE Transactions on Visualization and Computer Graphics human computer interaction hypothesis testing visual evidence visual knowledge discovery visual knowledge representation 2012 infovis12--264 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Spatial Text Visualization Using Automatic Typographic Maps. We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region. Afzal, S. Ebert, D.S. Elmqvist, N. Jang, Y. Maciejewski, R. geographic text InfoVis cities and towns data visualization geospatial analysis rendering (computer graphics) spatial databases IEEE Transactions on Visualization and Computer Graphics geovisualization label placement spatial data text visualization 2012 infovis12--265 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Stacking-Based Visualization of Trajectory Attribute Data. Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well. Andrienko, G. Andrienko, N. Schumann, H. Tominski, C. color dynamic query experiment graph navigation InfoVis data visualization image color analysis navigation trajectory IEEE Transactions on Visualization and Computer Graphics exploratory analysis interaction spatio-temporal data trajectory attribute data visualization 2012 infovis12--271 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Taxonomy-Based Glyph Design -- with a Case Study on Visualizing Workflows of Biological Experiments. Glyph-based visualization can offer elegant and concise presentation of multivariate information while enhancing speed and ease in visual search experienced by users. As with icon designs, glyphs are usually created based on the designers' experience and intuition, often in a spontaneous manner. Such a process does not scale well with the requirements of applications where a large number of concepts are to be encoded using glyphs. To alleviate such limitations, we propose a new systematic process for glyph design by exploring the parallel between the hierarchy of concept categorization and the ordering of discriminative capacity of visual channels. We examine the feasibility of this approach in an application where there is a pressing need for an efficient and effective means to visualize workflows of biological experiments. By processing thousands of workflow records in a public archive of biological experiments, we demonstrate that a cost-effective glyph design can be obtained by following a process of formulating a taxonomy with the aid of computation, identifying visual channels hierarchically, and defining application-specific abstraction and metaphors. Chen, M. Davies, J. Maguire, E. Rocca-Serra, P. Sansone, S.-A. case study glyph hierarchy taxonomy InfoVis data visualization glyph design IEEE Transactions on Visualization and Computer Graphics bioinformatics visualization design methodologies glyph-based techniques taxonomies 2012 infovis12--272 10/16/2012 IEEE Transactions on Visualization and Computer Graphics The DeepTree Exhibit: Visualizing the Tree of Life to Facilitate Informal Learning. In this paper, we present the DeepTree exhibit, a multi-user, multi-touch interactive visualization of the Tree of Life. We developed DeepTree to facilitate collaborative learning of evolutionary concepts. We will describe an iterative process in which a team of computer scientists, learning scientists, biologists, and museum curators worked together throughout design, development, and evaluation. We present the importance of designing the interactions and the visualization hand-in-hand in order to facilitate active learning. The outcome of this process is a fractal-based tree layout that reduces visual complexity while being able to capture all life on earth; a custom rendering and navigation engine that prioritizes visual appeal and smooth fly-through; and a multi-user interface that encourages collaborative exploration while offering guided discovery. We present an evaluation showing that the large dataset encouraged free exploration, triggers emotional responses, and facilitates visitor engagement and informal learning. Block, F. Diamond, J. Evans, E.M. Horn, M.S. Phillips, B.C. Shen, C. evaluation navigation InfoVis collaboration data visualization information science layout navigation phylogeny rendering (computer graphics) IEEE Transactions on Visualization and Computer Graphics collaborative learning informal science education large tree visualizations multi-touch interaction 2012 infovis12--275 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Understanding Pen and Touch Interaction for Data Exploration on Interactive Whiteboards. Current interfaces for common information visualizations such as bar graphs, line graphs, and scatterplots usually make use of the WIMP (Windows, Icons, Menus and a Pointer) interface paradigm with its frequently discussed problems of multiple levels of indirection via cascading menus, dialog boxes, and control panels. Recent advances in interface capabilities such as the availability of pen and touch interaction challenge us to re-think this and investigate more direct access to both the visualizations and the data they portray. We conducted a Wizard of Oz study to explore applying pen and touch interaction to the creation of information visualization interfaces on interactive whiteboards without implementing a plethora of recognizers. Our wizard acted as a robust and flexible pen and touch recognizer, giving participants maximum freedom in how they interacted with the system. Based on our qualitative analysis of the interactions our participants used, we discuss our insights about pen and touch interactions in the context of learnability and the interplay between pen and touch gestures. We conclude with suggestions for designing pen and touch enabled interactive visualization interfaces. Carpendale, S. Henry Riche, N. Johns, P. Lee, B. Walny, J. interaction InfoVis context awareness data visualization sociology statistical analysis writing IEEE Transactions on Visualization and Computer Graphics data exploration interaction pen and touch whiteboard wizard of oz 2012 infovis12--279 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visual Semiotics & Uncertainty Visualization: An Empirical Study. This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results. Gahegan, M. Li, B. MacEachren, A.M. O'Brien, J. Roth, R.E. Swingley, D. uncertainty InfoVis semiotics syntactics uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics semiotics uncertainty categories uncertainty visualization visual variables 2012 infovis12--285 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visualizing Flow of Uncertainty through Analytical Processes. Uncertainty can arise in any stage of a visual analytics process, especially in data-intensive applications with a sequence of data transformations. Additionally, throughout the process of multidimensional, multivariate data analysis, uncertainty due to data transformation and integration may split, merge, increase, or decrease. This dynamic characteristic along with other features of uncertainty pose a great challenge to effective uncertainty-aware visualization. This paper presents a new framework for modeling uncertainty and characterizing the evolution of the uncertainty information through analytical processes. Based on the framework, we have designed a visual metaphor called uncertainty flow to visually and intuitively summarize how uncertainty information propagates over the whole analysis pipeline. Our system allows analysts to interact with and analyze the uncertainty information at different levels of detail. Three experiments were conducted to demonstrate the effectiveness and intuitiveness of our design. Ma, K.-L. Wu, Y. Yuan, G. uncertainty visual analytics InfoVis covariance matrix data visualization ellipsoids uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics error ellipsoids uncertainty fusion uncertainty propagation uncertainty quantification uncertainty visualization 2012 infovis12--286 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations. The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system. Bhatele, A. Bremer, P.-T. Gamblin, T. Isaacs, K.E. Landge, A.G. Langer, S.H. Levine, J.A. Pascucci, V. Schulz, M. case study hardware insight network InfoVis computational modeling data visualization hardware layout network topology performance evaluation supercomputers IEEE Transactions on Visualization and Computer Graphics network traffic visualization performance analysis projected graph layouts 2012 infovis12--288 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visualizing Student Histories Using Clustering and Composition. While intuitive time-series visualizations exist for common datasets, student course history data is difficult to represent using traditional visualization techniques due its concurrent nature. A visual composition process is developed and applied to reveal trends across various groupings. By working closely with educators, analytic strategies and techniques are developed to leverage the visualization composition to reveal unknown trends in the data. Furthermore, clustering algorithms are developed to group common course-grade histories for further analysis. Lastly, variations of the composition process are implemented to reveal subtle differences in the underlying data. These analytic tools and techniques enabled educators to confirm expected trends and to discover new ones. Rheingans, P. Trimm, D. desJardins, M. clustering history InfoVis data visualization history image color analysis market research trajectory IEEE Transactions on Visualization and Computer Graphics aggregate visualization clustering student performance analysis visualization composition 2012 infovis12--291 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time. When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, "Whisper", for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild. Cao, N. Lazer, D. Lin, Y. Liu, S. Qu, H. Sun, X. social InfoVis diffusion processes media monitoring real-time systems social network services twitter IEEE Transactions on Visualization and Computer Graphics contagion information diffusion information visualization microblogging social media spatiotemporal patterns 2012 infovis13--119 10/16/2013 IEEE Transactions on Visualization and Computer Graphics A Deeper Understanding of Sequence in Narrative Visualization. Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence. Adar, E. Drucker, S.M. Fisher, D. Henry Riche, N. Hullman, J. Lee, B. graph InfoVis data visualization encoding linear programming parallel processing sequential analysis IEEE Transactions on Visualization and Computer Graphics data storytelling narrative structure narrative visualization 2013 infovis13--120 10/16/2013 IEEE Transactions on Visualization and Computer Graphics A Design Space of Visualization Tasks. Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it. Heitzler, M. Nocke, T. Schulz, H. Schumann, H. InfoVis data visualization market research meteorology taxonomy IEEE Transactions on Visualization and Computer Graphics climate impact research design space task taxonomy visualization recommendation 2013 infovis13--122 10/16/2013 IEEE Transactions on Visualization and Computer Graphics A Model for Structure-Based Comparison of Many Categories in Small-Multiple Displays. Many application domains deal with multi-variate data that consist of both categorical and numerical information. Smallmultiple displays are a powerful concept for comparing such data by juxtaposition. For comparison by overlay or by explicit encoding of computed differences, however, a specification of references is necessary. In this paper, we present a formal model for defining semantically meaningful comparisons between many categories in a small-multiple display. Based on pivotized data that are hierarchically partitioned by the categories assigned to the x and y axis of the display, we propose two alternatives for structure-based comparison within this hierarchy. With an absolute reference specification, categories are compared to a fixed reference category. With a relative reference specification, in contrast, a semantic ordering of the categories is considered when comparing them either to the previous or subsequent category each. Both reference specifications can be defined at multiple levels of the hierarchy (including aggregated summaries), enabling a multitude of useful comparisons. We demonstrate the general applicability of our model in several application examples using different visualizations that compare data by overlay or explicit encoding of differences. Berger, W. Gröller, M.E. Kehrer, J. Piringer, H. categorical hierarchy InfoVis computational modeling data visualization displays encoding IEEE Transactions on Visualization and Computer Graphics categorical data comparative visualization small-multiple displays trellis displays 2013 infovis13--124 10/16/2013 IEEE Transactions on Visualization and Computer Graphics A Multi-Level Typology of Abstract Visualization Tasks. The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography. Brehmer, M. Munzner, T. case study evaluation interaction InfoVis encoding modeling qualitative evaluations topology IEEE Transactions on Visualization and Computer Graphics qualitative evaluation task and requirements analysis typology visualization models 2013 infovis13--130 10/16/2013 IEEE Transactions on Visualization and Computer Graphics An Empirically-Derived Taxonomy of Interaction Primitives for Interactive Cartography and Geovisualization. Proposals to establish a 'science of interaction' have been forwarded from Information Visualization and Visual Analytics, as well as Cartography, Geovisualization, and GIScience. This paper reports on two studies to contribute to this call for an interaction science, with the goal of developing a functional taxonomy of interaction primitives for map-based visualization. A semi-structured interview study first was conducted with 21 expert interactive map users to understand the way in which map-based visualizations currently are employed. The interviews were transcribed and coded to identify statements representative of either the task the user wished to accomplish (i.e., objective primitives) or the interactive functionality included in the visualization to achieve this task (i.e., operator primitives). A card sorting study then was conducted with 15 expert interactive map designers to organize these example statements into logical structures based on their experience translating client requests into interaction designs. Example statements were supplemented with primitive definitions in the literature and were separated into two sorting exercises: objectives and operators. The objective sort suggested five objectives that increase in cognitive sophistication (identify, compare, rank, associate, & delineate), but exhibited a large amount of variation across participants due to consideration of broader user goals (procure, predict, & prescribe) and interaction operands (space-alone, attributes-in-space, & space-in-time; elementary & general). The operator sort suggested five enabling operators (import, export, save, edit, & annotate) and twelve work operators (reexpress, arrange, sequence, resymbolize, overlay, pan, zoom, reproject, search, filter, retrieve, & calculate). This taxonomy offers an empirically-derived and ecologically-valid structure to inform future research and design on interaction. Roth, R.E. filter geovisualization interaction taxonomy visual analytics zoom InfoVis cartography geophysical measurements object recognition search problems IEEE Transactions on Visualization and Computer Graphics geovisualization interaction primitives interaction techniques interactive maps science of interaction 2013 infovis13--134 10/16/2013 IEEE Transactions on Visualization and Computer Graphics An Interaction Model for Visualizations Beyond The Desktop. We present an interaction model for beyond-desktop visualizations that combines the visualization reference model with the instrumental interaction paradigm. Beyond-desktop visualizations involve a wide range of emerging technologies such as wall-sized displays, 3D and shape-changing displays, touch and tangible input, and physical information visualizations. While these technologies allow for new forms of interaction, they are often studied in isolation. New conceptual models are needed to build a coherent picture of what has been done and what is possible. We describe a modified pipeline model where raw data is processed into a visualization and then rendered into the physical world. Users can explore or change data by directly manipulating visualizations or through the use of instruments. Interactions can also take place in the physical world outside the visualization system, such as when using locomotion to inspect a large scale visualization. Through case studies we illustrate how this model can be used to describe both conventional and unconventional interactive visualization systems, and compare different design alternatives. Dragicevic, P. Jansen, Y. interaction InfoVis data visualization pipelines rendering (computer graphics) three-dimensional displays IEEE Transactions on Visualization and Computer Graphics information visualization interaction model notational system physical visualization 2013 infovis13--137 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Automatic Layout of Structured Hierarchical Reports. Domain-specific database applications tend to contain a sizable number of table-, form-, and report-style views that must each be designed and maintained by a software developer. A significant part of this job is the necessary tweaking of low-level presentation details such as label placements, text field dimensions, list or table styles, and so on. In this paper, we present a horizontally constrained layout management algorithm that automates the display of structured hierarchical data using the traditional visual idioms of hand-designed database UIs: tables, multi-column forms, and outline-style indented lists. We compare our system with pure outline and nested table layouts with respect to space efficiency and readability, the latter with an online user study on 27 subjects. Our layouts are 3.9 and 1.6 times more compact on average than outline layouts and horizontally unconstrained table layouts, respectively, and are as readable as table layouts even for large datasets. Bakke, E. Karger, D.R. Miller, R.C. database text user study InfoVis data visualization layout xml IEEE Transactions on Visualization and Computer Graphics hierarchy data layout management nested relations tabular data 2013 infovis13--140 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Common Angle Plots as Perception-True Visualizations of Categorical Associations. Visualizations are great tools of communications-they summarize findings and quickly convey main messages to our audience. As designers of charts we have to make sure that information is shown with a minimum of distortion. We have to also consider illusions and other perceptual limitations of our audience. In this paper we discuss the effect and strength of the line width illusion, a Muller-Lyer type illusion, on designs related to displaying associations between categorical variables. Parallel sets and hammock plots are both affected by line width illusions. We introduce the common-angle plot as an alternative method for displaying categorical data in a manner that minimizes the effect from perceptual illusions. Results from user studies both highlight the need for addressing line-width illusions in displays and provide evidence that common angle charts successfully resolve this issue. Hofmann, H. Vendettuoli, M. categorical distortion perception InfoVis biochemistry biological cells data visualization parallel processing IEEE Transactions on Visualization and Computer Graphics data visualization hammock plots high-dimensional displays linewidth illusion muller-lyer illusion parallel sets 2013 infovis13--145 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Creative User-Centered Visualization Design for Energy Analysts and Modelers. We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open - enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design. Dillingham, I. Dove, G. Duffy, A. Dykes, J. Goodwin, S. Jones, S. Kachkaev, A. Slingsby, A. Wood, J. InfoVis data models data visualization home appliances prototypes smart homes IEEE Transactions on Visualization and Computer Graphics creativity techniques data visualization energy consumption smart home user-centered design 2013 infovis13--149 10/16/2013 IEEE Transactions on Visualization and Computer Graphics DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation. Visualization of dynamically changing networks (graphs) is a significant challenge for researchers. Previous work has experimentally compared animation, small multiples, and other techniques, and found trade-offs between these. One potential way to avoid such trade-offs is to combine previous techniques in a hybrid visualization. We present two taxonomies of visualizations of dynamic graphs: one of non-hybrid techniques, and one of hybrid techniques. We also describe a prototype, called DiffAni, that allows a graph to be visualized as a sequence of three kinds of tiles: diff tiles that show difference maps over some time interval, animation tiles that show the evolution of the graph over some time interval, and small multiple tiles that show the graph state at an individual time slice. This sequence of tiles is ordered by time and covers all time slices in the data. An experimental evaluation of DiffAni shows that our hybrid approach has advantages over non-hybrid techniques in certain cases. McGuffin, M.J. Rufiange, S. animation evaluation graph small multiples InfoVis animation computer graphics prototypes IEEE Transactions on Visualization and Computer Graphics animation difference map dynamic networks evolution hybrid visualization taxonomy 2013 infovis13--150 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data. For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions. Guo, C. Ren, D. Wang, Z. Yuan, X. brushing high-dimensional data interaction matrix scatterplot InfoVis algorithm design and analysis clustering algorithms correlation data visualization image color analysis IEEE Transactions on Visualization and Computer Graphics hierarchical visualization high dimensional data matrix sub-dimensional space subspace tree user interaction 2013 infovis13--151 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Edge Compression Techniques for Visualization of Dense Directed Graphs. We explore the effectiveness of visualizing dense directed graphs by replacing individual edges with edges connected to 'modules'-or groups of nodes-such that the new edges imply aggregate connectivity. We only consider techniques that offer a lossless compression: that is, where the entire graph can still be read from the compressed version. The techniques considered are: a simple grouping of nodes with identical neighbor sets; Modular Decomposition which permits internal structure in modules and allows them to be nested; and Power Graph Analysis which further allows edges to cross module boundaries. These techniques all have the same goal-to compress the set of edges that need to be rendered to fully convey connectivity-but each successive relaxation of the module definition permits fewer edges to be drawn in the rendered graph. Each successive technique also, we hypothesize, requires a higher degree of mental effort to interpret. We test this hypothetical trade-off with two studies involving human participants. For Power Graph Analysis we propose a novel optimal technique based on constraint programming. This enables us to explore the parameter space for the technique more precisely than could be achieved with a heuristic. Although applicable to many domains, we are motivated by-and discuss in particular-the application to software dependency analysis. Dwyer, T. Henry Riche, N. Marriott, K. Mears, C. graph InfoVis computer graphics edge detection modular construction IEEE Transactions on Visualization and Computer Graphics directed graphs modular decomposition networks power graph analysis 2013 infovis13--153 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices. To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process. Munzner, T. Sedlmair, M. Tory, M. cluster color dimension reduction high-dimensional data scatterplot usability InfoVis data analysis data visualization encoding principal component analysis three-dimensional displays IEEE Transactions on Visualization and Computer Graphics dimensionality reduction quantitative study scatterplots 2013 infovis13--154 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets. Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst's task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Gratzl, S. Kalkofen, D. Lex, A. Partl, C. Pfister, H. Schmalstieg, D. Streit, M. Wassermann, A.-M. graph network InfoVis bioinformatics biological system modeling context awareness data visualization drugs portals IEEE Transactions on Visualization and Computer Graphics biological networks biomolecular data graphs pathway visualization subsets 2013 infovis13--155 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Evaluation of Filesystem Provenance Visualization Tools. Having effective visualizations of filesystem provenance data is valuable for understanding its complex hierarchical structure. The most common visual representation of provenance data is the node-link diagram. While effective for understanding local activity, the node-link diagram fails to offer a high-level summary of activity and inter-relationships within the data. We present a new tool, InProv, which displays filesystem provenance with an interactive radial-based tree layout. The tool also utilizes a new time-based hierarchical node grouping method for filesystem provenance data we developed to match the user's mental model and make data exploration more intuitive. We compared InProv to a conventional node-link based tool, Orbiter, in a quantitative evaluation with real users of filesystem provenance data including provenance data experts, IT professionals, and computational scientists. We also compared in the evaluation our new node grouping method to a conventional method. The results demonstrate that InProv results in higher accuracy in identifying system activity than Orbiter with large complex data sets. The results also show that our new time-based hierarchical node grouping method improves performance in both tools, and participants found both tools significantly easier to use with the new time-based node grouping method. Subjective measures show that participants found InProv to require less mental activity, less physical activity, less work, and is less stressful to use. Our study also reveals one of the first cases of gender differences in visualization; both genders had comparable performance with InProv, but women had a significantly lower average accuracy (56%) compared to men (70%) with Orbiter. Borkin, M. Boyd, M. Gajos, K. Macko, P. Pfister, H. Seltzer, M. Yeh, C.S. evaluation radial InfoVis context awareness data visualization encoding layout IEEE Transactions on Visualization and Computer Graphics gender differences graph/network data hierarchy data provenance data quantitative evaluation 2013 infovis13--160 10/16/2013 IEEE Transactions on Visualization and Computer Graphics GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data. Scatterplot matrices (SPLOMs), parallel coordinates, and glyphs can all be used to visualize the multiple continuous variables (i.e., dependent variables or measures) in multidimensional multivariate data. However, these techniques are not well suited to visualizing many categorical variables (i.e., independent variables or dimensions). To visualize multiple categorical variables, 'hierarchical axes' that 'stack dimensions' have been used in systems like Polaris and Tableau. However, this approach does not scale well beyond a small number of categorical variables. Emerson et al. [8] extend the matrix paradigm of the SPLOM to simultaneously visualize several categorical and continuous variables, displaying many kinds of charts in the matrix depending on the kinds of variables involved. We propose a variant of their technique, called the Generalized Plot Matrix (GPLOM). The GPLOM restricts Emerson et al.'s technique to only three kinds of charts (scatterplots for pairs of continuous variables, heatmaps for pairs of categorical variables, and barcharts for pairings of categorical and continuous variable), in an effort to make it easier to understand. At the same time, the GPLOM extends Emerson et al.'s work by demonstrating interactive techniques suited to the matrix of charts. We discuss the visual design and interactive features of our GPLOM prototype, including a textual search feature allowing users to quickly locate values or variables by name. We also present a user study that compared performance with Tableau and our GPLOM prototype, that found that GPLOM is significantly faster in certain cases, and not significantly slower in other cases. Im, J. Leung, R. McGuffin, M.J. categorical matrix parallel coordinates scatterplot user study InfoVis data visualization prototypes visual databases IEEE Transactions on Visualization and Computer Graphics business intelligence database visualization databaseoverview high-dimensional data mdmv multidimensional data parallel coordinates relational data scatterplot matrix tabular data user interfaces 2013 infovis13--163 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Hybrid-Image Visualization for Large Viewing Environments. We present a first investigation into hybrid-image visualization for data analysis in large-scale viewing environments. Hybrid-image visualizations blend two different visual representations into a single static view, such that each representation can be perceived at a different viewing distance. Our work is motivated by data analysis scenarios that incorporate one or more displays with sufficiently large size and resolution to be comfortably viewed by different people from various distances. Hybrid-image visualizations can be used, in particular, to enhance overview tasks from a distance and detail-in-context tasks when standing close to the display. By using a perception-based blending approach, hybrid-image visualizations make two full-screen visualizations accessible without tracking viewers in front of a display. We contribute a design space, discuss the perceptual rationale for our work, provide examples, and introduce a set of techniques and tools to aid the design of hybrid-image visualizations. Bezerianos, A. Dragicevic, P. Fekete, J.-D. Isenberg, P. Willett, W. overview perception InfoVis data visualization encoding frequency-domain analysis image color analysis IEEE Transactions on Visualization and Computer Graphics collaboration hybrid images large displays multi-scale visualization 2013 infovis13--166 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Information Visualization and Proxemics: Design Opportunities and Empirical Findings. People typically interact with information visualizations using a mouse. Their physical movement, orientation, and distance to visualizations are rarely used as input. We explore how to use such spatial relations among people and visualizations (i.e., proxemics) to drive interaction with visualizations, focusing here on the spatial relations between a single user and visualizations on a large display. We implement interaction techniques that zoom and pan, query and relate, and adapt visualizations based on tracking of users' position in relation to a large high-resolution display. Alternative prototypes are tested in three user studies and compared with baseline conditions that use a mouse. Our aim is to gain empirical data on the usefulness of a range of design possibilities and to generate more ideas. Among other things, the results show promise for changing zoom level or visual representation with the user's physical distance to a large display. We discuss possible benefits and potential issues to avoid when designing information visualizations that use proxemics. Hornbæk, K. Jakobsen, M.R. Knudsen, S. Sahlemariam Haile, Y. interaction large display zoom InfoVis data visualization encoding information filters navigation IEEE Transactions on Visualization and Computer Graphics distance information visualization large displays movement orientation proxemics user study user tracking 2013 infovis13--170 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Interactive Visualizations on Large and Small Displays: The Interrelation of Display Size, Information Space, and Scale. In controlled experiments on the relation of display size (i.e., the number of pixels) and the usability of visualizations, the size of the information space can either be kept constant or varied relative to display size. Both experimental approaches have limitations. If the information space is kept constant then the scale ratio between an overview of the entire information space and the lowest zoom level varies, which can impact performance; if the information space is varied then the scale ratio is kept constant, but performance cannot be directly compared. In other words, display size, information space, and scale ratio are interrelated variables. We investigate this relation in two experiments with interfaces that implement classic information visualization techniques-focus+context, overview+detail, and zooming-for multi-scale navigation in maps. Display size varied between 0.17, 1.5, and 13.8 megapixels. Information space varied relative to display size in one experiment and was constant in the other. Results suggest that for tasks where users navigate targets that are visible at all map scales the interfaces do not benefit from a large display: With a constant map size, a larger display does not improve performance with the interfaces; with map size varied relative to display size, participants found interfaces harder to use with a larger display and task completion times decrease only when they are normalized to compensate for the increase in map size. The two experimental approaches show different interaction effects between display size and interface. In particular, focus+context performs relatively worse at a large display size with variable map size, and relatively worse at a small display size with a fixed map size. Based on a theoretical analysis of the interaction with the visualization techniques, we examine individual task actions empirically so as to understand the relative impact of display size and scale ratio on the visualization techniques' p- rformance and to discuss differences between the two experimental approaches. Hornbæk, K. Jakobsen, M.R. experiment focus+context interaction large display navigation overview usability zoom zooming InfoVis aerospace electronics data visualization interactive systems monitoring navigation IEEE Transactions on Visualization and Computer Graphics experimental method information visualization interaction techniques multi-scale navigation user studies 2013 infovis13--173 10/16/2013 IEEE Transactions on Visualization and Computer Graphics LineUp: Visual Analysis of Multi-Attribute Rankings. Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time. Gehlenborg, N. Gratzl, S. Lex, A. Pfister, H. Streit, M. insight InfoVis data visualization encoding histograms rankings scalability IEEE Transactions on Visualization and Computer Graphics multi-attribute multi-faceted multifactorial ranking ranking visualization scoring stacked bar charts 2013 infovis13--179 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Nanocubes for Real-Time Exploration of Spatiotemporal Datasets. Consider real-time exploration of large multidimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in the data? Answering these questions requires aggregation over arbitrary regions of the domain and attributes of the data. Many relational databases implement the well-known data cube aggregation operation, which in a sense precomputes every possible aggregate query over the database. Data cubes are sometimes assumed to take a prohibitively large amount of space, and to consequently require disk storage. In contrast, we show how to construct a data cube that fits in a modern laptop's main memory, even for billions of entries; we call this data structure a nanocube. We present algorithms to compute and query a nanocube, and show how it can be used to generate well-known visual encodings such as heatmaps, histograms, and parallel coordinate plots. When compared to exact visualizations created by scanning an entire dataset, nanocube plots have bounded screen error across a variety of scales, thanks to a hierarchical structure in space and time. We demonstrate the effectiveness of our technique on a variety of real-world datasets, and present memory, timing, and network bandwidth measurements. We find that the timings for the queries in our examples are dominated by network and user-interaction latencies. Klosowski, J.T. Lins, L. Scheidegger, C. database interaction network InfoVis androids data visualization encoding humanoid robots nanostructured materials spatiotemporal phenomena IEEE Transactions on Visualization and Computer Graphics data cube data structures interactive exploration 2013 infovis13--182 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Orthographic Star Coordinates. Star coordinates is a popular projection technique from an nD data space to a 2D/3D visualization domain. It is defined by setting n coordinate axes in the visualization domain. Since it generally defines an affine projection, strong distortions can occur: an nD sphere can be mapped to an ellipse of arbitrary size and aspect ratio. We propose to restrict star coordinates to orthographic projections which map an nD sphere of radius r to a 2D circle of radius r. We achieve this by formulating conditions for the coordinate axes to define orthographic projections, and by running a repeated non-linear optimization in the background of every modification of the coordinate axes. This way, we define a number of orthographic interaction concepts as well as orthographic data tour sequences: a scatterplot tour, a principle component tour, and a grand tour. All concepts are illustrated and evaluated with synthetic and real data. Lehmann, D.J. Theisel, H. interaction scatterplot InfoVis data visualization minimization nonlinear distortion principal component analysis three-dimensional displays IEEE Transactions on Visualization and Computer Graphics multivariate visualization start plot visual analytics 2013 infovis13--183 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Perception of Average Value in Multiclass Scatterplots. The visual system can make highly efficient aggregate judgements about a set of objects, with speed roughly independent of the number of objects considered. While there is a rich literature on these mechanisms and their ramifications for visual summarization tasks, this prior work rarely considers more complex tasks requiring multiple judgements over long periods of time, and has not considered certain critical aggregation types, such as the localization of the mean value of a set of points. In this paper, we explore these questions using a common visualization task as a case study: relative mean value judgements within multi-class scatterplots. We describe how the perception literature provides a set of expected constraints on the task, and evaluate these predictions with a large-scale perceptual study with crowd-sourced participants. Judgements are no harder when each set contains more points, redundant and conflicting encodings, as well as additional sets, do not strongly affect performance, and judgements are harder when using less salient encodings. These results have concrete ramifications for the design of scatterplots. Correll, M. Franconeri, S. Gleicher, M. Nothelfer, C. case study perception InfoVis color imaging encoding shape analysis visual systems IEEE Transactions on Visualization and Computer Graphics information visualization perceptual study psychophysics 2013 infovis13--184 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Radial Sets: Interactive Visual Analysis of Large Overlapping Sets. In many applications, data tables contain multi-valued attributes that often store the memberships of the table entities to multiple sets such as which languages a person masters, which skills an applicant documents, or which features a product comes with. With a growing number of entities, the resulting element-set membership matrix becomes very rich of information about how these sets overlap. Many analysis tasks targeted at set-typed data are concerned with these overlaps as salient features of such data. This paper presents Radial Sets, a novel visual technique to analyze set memberships for a large number of elements. Our technique uses frequency-based representations to enable quickly finding and analyzing different kinds of overlaps between the sets, and relating these overlaps to other attributes of the table entities. Furthermore, it enables various interactions to select elements of interest, find out if they are over-represented in specific sets or overlaps, and if they exhibit a different distribution for a specific attribute compared to the rest of the elements. These interactions allow formulating highly-expressive visual queries on the elements in terms of their set memberships and attribute values. As we demonstrate via two usage scenarios, Radial Sets enable revealing and analyzing a multitude of overlapping patterns between large sets, beyond the limits of state-of-the-art techniques. Aigner, W. Alsallakh, B. Hauser, H. Miksch, S. matrix radial InfoVis color imaging data visualization histograms interactive systems scalability IEEE Transactions on Visualization and Computer Graphics multi-valued attributes overlapping sets scalability set-typed data visualization technique 2013 infovis13--187 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Selecting the Aspect Ratio of a Scatter Plot Based on Its Delaunay Triangulation. Scatter plots are diagrams that visualize two-dimensional data as sets of points in the plane. They allow users to detect correlations and clusters in the data. Whether or not a user can accomplish these tasks highly depends on the aspect ratio selected for the plot, i.e., the ratio between the horizontal and the vertical extent of the diagram. We argue that an aspect ratio is good if the Delaunay triangulation of the scatter plot at this aspect ratio has some nice geometric property, e.g., a large minimum angle or a small total edge length. More precisely, we consider the following optimization problem. Given a set Q of points in the plane, find a scale factor s such that scaling the x-coordinates of the points in Q by s and the y-coordinates by 1=s yields a point set P(s) that optimizes a property of the Delaunay triangulation of P(s), over all choices of s. We present an algorithm that solves this problem efficiently and demonstrate its usefulness on real-world instances. Moreover, we discuss an empirical test in which we asked 64 participants to choose the aspect ratios of 18 scatter plots. We tested six different quality measures that our algorithm can optimize. In conclusion, minimizing the total edge length and minimizing what we call the 'uncompactness' of the triangles of the Delaunay triangulation yielded the aspect ratios that were most similar to those chosen by the participants in the test. Fink, M. Haunert, J. Spoerhase, J. Wolff, A. InfoVis approximation algorithms approximation methods atmospheric measurements data visualization market research particle measurements IEEE Transactions on Visualization and Computer Graphics aspect ratio delaunay triangulation scatter plot 2013 infovis13--191 10/16/2013 IEEE Transactions on Visualization and Computer Graphics SketchStory: Telling More Engaging Stories with Data through Freeform Sketching. Presenting and communicating insights to an audience-telling a story-is one of the main goals of data exploration. Even though visualization as a storytelling medium has recently begun to gain attention, storytelling is still underexplored in information visualization and little research has been done to help people tell their stories with data. To create a new, more engaging form of storytelling with data, we leverage and extend the narrative storytelling attributes of whiteboard animation with pen and touch interactions. We present SketchStory, a data-enabled digital whiteboard that facilitates the creation of personalized and expressive data charts quickly and easily. SketchStory recognizes a small set of sketch gestures for chart invocation, and automatically completes charts by synthesizing the visuals from the presenter-provided example icon and binding them to the underlying data. Furthermore, SketchStory allows the presenter to move and resize the completed data charts with touch, and filter the underlying data to facilitate interactive exploration. We conducted a controlled experiment for both audiences and presenters to compare SketchStory with a traditional presentation system, Microsoft PowerPoint. Results show that the audience is more engaged by presentations done with SketchStory than PowerPoint. Eighteen out of 24 audience participants preferred SketchStory to PowerPoint. Four out of five presenter participants also favored SketchStory despite the extra effort required for presentation. Kazi, R.H. Lee, B. Smith, G. animation experiment filter InfoVis animation data visualization filtering real-time systems rendering (computer graphics) IEEE Transactions on Visualization and Computer Graphics data presentation interaction pen and touch sketch storytelling visualization 2013 infovis13--192 10/16/2013 IEEE Transactions on Visualization and Computer Graphics SoccerStories: A Kick-off for Visual Soccer Analysis. This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow. Fekete, J.-D. Perin, C. Vuillemot, R. overview statistics InfoVis data visualization games layout navigation IEEE Transactions on Visualization and Computer Graphics sport analytics visual aggregation visual knowledge discovery visual knowledge representation 2013 infovis13--196 10/16/2013 IEEE Transactions on Visualization and Computer Graphics StoryFlow: Tracking the Evolution of Stories. Storyline visualizations, which are useful in many applications, aim to illustrate the dynamic relationships between entities in a story. However, the growing complexity and scalability of stories pose great challenges for existing approaches. In this paper, we propose an efficient optimization approach to generating an aesthetically appealing storyline visualization, which effectively handles the hierarchical relationships between entities over time. The approach formulates the storyline layout as a novel hybrid optimization approach that combines discrete and continuous optimization. The discrete method generates an initial layout through the ordering and alignment of entities, and the continuous method optimizes the initial layout to produce the optimal one. The efficient approach makes real-time interactions (e.g., bundling and straightening) possible, thus enabling users to better understand and track how the story evolves. Experiments and case studies are conducted to demonstrate the effectiveness and usefulness of the optimization approach. Liu, M. Liu, S. Liu, Y. Wei, E. Wu, Y. InfoVis heuristic algorithms layout motion pictures optimization white spaces IEEE Transactions on Visualization and Computer Graphics level-of-detail optimization story-telling visualization storylines user interactions 2013 infovis13--209 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization. Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query- or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy. Basole, R.C. Clear, T. Hu, M. Mehrotra, H. Stasko, J. business design study evaluation field study insight InfoVis companies data visualization ecosystems interactive systems mobile communication IEEE Transactions on Visualization and Computer Graphics business ecosystems design study interaction market research network visualization strategic analysis 2013 infovis13--210 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Using Concrete Scales: A Practical Framework for Effective Visual Depiction of Complex Measures. From financial statistics to nutritional values, we are frequently exposed to quantitative information expressed in measures of either extreme magnitudes or unfamiliar units, or both. A common practice used to comprehend such complex measures is to relate, re-express, and compare them through visual depictions using magnitudes and units that are easier to grasp. Through this practice, we create a new graphic composition that we refer to as a concrete scale. To the best of our knowledge, there are no design guidelines that exist for concrete scales despite their common use in communication, educational, and decision-making settings. We attempt to fill this void by introducing a novel framework that would serve as a practical guide for their analysis and design. Informed by a thorough analysis of graphic compositions involving complex measures and an extensive literature review of scale cognition mechanisms, our framework outlines the design space of various measure relations-specifically relations involving the re-expression of complex measures to more familiar concepts-and their visual representations as graphic compositions. Chevalier, F. Gali, G. Vuillemot, R. cognition financial statistics InfoVis complexity theory computer graphics measurement IEEE Transactions on Visualization and Computer Graphics concrete scale graphic composition scale cognition visual comparison visual notation 2013 infovis13--214 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Variant View: Visualizing Sequence Variants in their Gene Context. Scientists use DNA sequence differences between an individual's genome and a standard reference genome to study the genetic basis of disease. Such differences are called sequence variants, and determining their impact in the cell is difficult because it requires reasoning about both the type and location of the variant across several levels of biological context. In this design study, we worked with four analysts to design a visualization tool supporting variant impact assessment for three different tasks. We contribute data and task abstractions for the problem of variant impact assessment, and the carefully justified design and implementation of the Variant View tool. Variant View features an information-dense visual encoding that provides maximal information at the overview level, in contrast to the extensive navigation required by currently-prevalent genome browsers. We provide initial evidence that the tool simplified and accelerated workflows for these three tasks through three case studies. Finally, we reflect on the lessons learned in creating and refining data and task abstractions that allow for concise overviews of sprawling information spaces that can reduce or remove the need for the memory-intensive use of navigation. Ferstay, J.A. Munzner, T. Nielsen, C.B. design study navigation overview InfoVis bioinformatics browsers context awareness databases design methodology genomics sequential analysis IEEE Transactions on Visualization and Computer Graphics bioinformatics design study genetic variants information visualization 2013 infovis13--225 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs. This paper is concerned with the creation of 'macros' in workflow visualization as a support tool to increase the efficiency of data curation tasks. We propose computation of candidate macros based on their usage in large collections of workflows in data repositories. We describe an efficient algorithm for extracting macro motifs from workflow graphs. We discovered that the state transition information, used to identify macro candidates, characterizes the structural pattern of the macro and can be harnessed as part of the visual design of the corresponding macro glyph. This facilitates partial automation and consistency in glyph design applicable to a large set of macro glyphs. We tested this approach against a repository of biological data holding some 9,670 workflows and found that the algorithmically generated candidate macros are in keeping with domain expert expectations. Chen, M. Davies, J. Maguire, E. Rocca-Serra, P. Sansone, S.-A. glyph InfoVis algorithm design and analysis biological system modeling data visualization semantics IEEE Transactions on Visualization and Computer Graphics glyph generation glyph-based visualization motif detection state-transition-based algorithm workflow visualization 2013 infovis13--227 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Sedimentation. We introduce Visual Sedimentation, a novel design metaphor for visualizing data streams directly inspired by the physical process of sedimentation. Visualizing data streams (e. g., Tweets, RSS, Emails) is challenging as incoming data arrive at unpredictable rates and have to remain readable. For data streams, clearly expressing chronological order while avoiding clutter, and keeping aging data visible, are important. The metaphor is drawn from the real-world sedimentation processes: objects fall due to gravity, and aggregate into strata over time. Inspired by this metaphor, data is visually depicted as falling objects using a force model to land on a surface, aggregating into strata over time. In this paper, we discuss how this metaphor addresses the specific challenge of smoothing the transition between incoming and aging data. We describe the metaphor's design space, a toolkit developed to facilitate its implementation, and example applications to a range of case studies. We then explore the generative capabilities of the design space through our toolkit. We finally illustrate creative extensions of the metaphor when applied to real streams of data. Fekete, J.-D. Huron, S. Vuillemot, R. toolkit InfoVis data visualization design methodology real-time systems sediments IEEE Transactions on Visualization and Computer Graphics data stream design dynamic data dynamic visualization information visualization metaphor real time 2013 infovis13--230 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visualization of Shape Motions in Shape Space. Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying three-dimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods. Hua, J. Taimouri, V. InfoVis atomic measurements biomedical monitoring cardiology data visualization heart level measurement shape analysis IEEE Transactions on Visualization and Computer Graphics comparative visualization left ventricle diagnosis medial surface shape space 2013 infovis13--231 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visualizing Change over Time Using Dynamic Hierarchies: TreeVersity2 and the StemView. To analyze data such as the US Federal Budget or characteristics of the student population of a University it is common to look for changes over time. This task can be made easier and more fruitful if the analysis is performed by grouping by attributes, such as by Agencies, Bureaus and Accounts for the Budget, or Ethnicity, Gender and Major in a University. We present TreeVersity2, a web based interactive data visualization tool that allows users to analyze change in datasets by creating dynamic hierarchies based on the data attributes. TreeVersity2 introduces a novel space filling visualization (StemView) to represent change in trees at multiple levels - not just at the leaf level. With this visualization users can explore absolute and relative changes, created and removed nodes, and each node's actual values, while maintaining the context of the tree. In addition, TreeVersity2 provides overviews of change over the entire time period, and a reporting tool that lists outliers in textual form, which helps users identify the major changes in the data without having to manually setup filters. We validated TreeVersity2 with 12 case studies with organizations as diverse as the National Cancer Institute, Federal Drug Administration, Department of Transportation, Office of the Bursar of the University of Maryland, or eBay. Our case studies demonstrated that TreeVersity2 is flexible enough to be used in different domains and provide useful insights for the data owners. A TreeVersity2 demo can be found at https://treeversity.cattlab.umd.edu. Guerra-Gomez, J. Pack, M.L. Plaisant, C. Shneiderman, B. hierarchies InfoVis context awareness data visualization image color analysis topology IEEE Transactions on Visualization and Computer Graphics information visualization tree comparison 2013 infovis13--232 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visualizing Fuzzy Overlapping Communities in Networks. An important feature of networks for many application domains is their community structure. This is because objects within the same community usually have at least one property in common. The investigation of community structure can therefore support the understanding of object attributes from the network topology alone. In real-world systems, objects may belong to several communities at the same time, i.e., communities can overlap. Analyzing fuzzy community memberships is essential to understand to what extent objects contribute to different communities and whether some communities are highly interconnected. We developed a visualization approach that is based on node-link diagrams and supports the investigation of fuzzy communities in weighted undirected graphs at different levels of detail. Starting with the network of communities, the user can continuously drill down to the network of individual nodes and finally analyze the membership distribution of nodes of interest. Our approach uses layout strategies and further visual mappings to graphically encode the fuzzy community memberships. The usefulness of our approach is illustrated by two case studies analyzing networks of different domains: social networking and biological interactions. The case studies showed that our layout and visualization approach helps investigate fuzzy overlapping communities. Fuzzy vertices as well as the different communities to which they belong can be easily identified based on node color and position. Reinhardt, T. Vehlow, C. Weiskopf, D. color network social InfoVis communities data visualization fuzzy methods image color analysis layout uncertainty IEEE Transactions on Visualization and Computer Graphics fuzzy clustering graph visualization overlapping community visualization uncertainty visualization 2013 infovis13--233 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visualizing Request-Flow Comparison to Aid Performance Diagnosis in Distributed Systems. Distributed systems are complex to develop and administer, and performance problem diagnosis is particularly challenging. When performance degrades, the problem might be in any of the system's many components or could be a result of poor interactions among them. Recent research efforts have created tools that automatically localize the problem to a small number of potential culprits, but research is needed to understand what visualization techniques work best for helping distributed systems developers understand and explore their results. This paper compares the relative merits of three well-known visualization approaches (side-by-side, diff, and animation) in the context of presenting the results of one proven automated localization technique called request-flow comparison. Via a 26-person user study, which included real distributed systems developers, we identify the unique benefits that each approach provides for different problem types and usage modes. Ganger, G.R. Mazurek, M.L. Sambasivan, R.R. Shafer, I. animation user study InfoVis distributed processing human factors layout training IEEE Transactions on Visualization and Computer Graphics distributed systems human factors problem diagnosis visualization 2013 infovis13--234 10/16/2013 IEEE Transactions on Visualization and Computer Graphics What Makes a Visualization Memorable?. An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: 'What makes a visualization memorable?' We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon's Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations. Borkin, M. Bylinskii, Z. Isola, P. Oliva, A. Pfister, H. Sunkavalli, S. Vo, A.A. cognition color graph InfoVis data visualization encoding information technology taxonomy IEEE Transactions on Visualization and Computer Graphics information visualization memorability visualization taxonomy 2013 infovis14--2346984 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Principled Way of Assessing Visualization Literacy. We describe a method for assessing the visualization literacy (VL) of a user. Assessing how well people understand visualizations has great value for research (e. g., to avoid confounds), for design (e. g., to best determine the capabilities of an audience), for teaching (e. g., to assess the level of new students), and for recruiting (e. g., to assess the level of interviewees). This paper proposes a method for assessing VL based on Item Response Theory. It describes the design and evaluation of two VL tests for line graphs, and presents the extension of the method to bar charts and scatterplots. Finally, it discusses the reimplementation of these tests for fast, effective, and scalable web-based use. Bertini, E. Boy, J. Fekete, J.-D. Rensink, R.A. evaluation theory InfoVis data mining data models data visualization encoding market research IEEE Transactions on Visualization and Computer Graphics item response theory literacy rasch model visualization literacy 2014 infovis14--2352953 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Activity Sculptures: Exploring the Impact of Physical Visualizations on Running Activity. Data sculptures are a promising type of visualizations in which data is given a physical form. In the past, they have mostly been used for artistic, communicative or educational purposes, and designers of data sculptures argue that in such situations, physical visualizations can be more enriching than pixel-based visualizations. We present the design of Activity Sculptures: data sculptures of running activity. In a three-week field study we investigated the impact of the sculptures on 14 participants' running activity, the personal and social behaviors generated by the sculptures, as well as participants' experiences when receiving these individual physical tokens generated from the specific data of their runs. The physical rewards generated curiosity and personal experimentation but also social dynamics such as discussion on runs or envy/competition. We argue that such passive (or calm) visualizations can complement nudging and other mechanisms of persuasion with a more playful and reflective look at ones' activity. Butz, A. Khot, R.A. Sauka, F. Stusak, S. Tabard, A. field study pixel social InfoVis data visualization mobile communication printing solid modeling three-dimensional displays IEEE Transactions on Visualization and Computer Graphics activity sculptures behavioral change data sculptures physical activity physical visualizations 2014 infovis14--2346325 11/12/2014 IEEE Transactions on Visualization and Computer Graphics An Algebraic Process for Visualization Design. We present a model of visualization design based on algebraic considerations of the visualization process. The model helps characterize visual encodings, guide their design, evaluate their effectiveness, and highlight their shortcomings. The model has three components: the underlying mathematical structure of the data or object being visualized, the concrete representation of the data in a computer, and (to the extent possible) a mathematical description of how humans perceive the visualization. Because we believe the value of our model lies in its practical application, we propose three general principles for good visualization design. We work through a collection of examples where our model helps explain the known properties of existing visualizations methods, both good and not-so-good, as well as suggesting some novel methods. We describe how to use the model alongside experimental user studies, since it can help frame experiment outcomes in an actionable manner. Exploring the implications and applications of our model and its design principles should provide many directions for future visualization research. Kindlmann, G.L. Scheidegger, C. experiment InfoVis algebra data models data visualization design methodology image color analysis mathematical model IEEE Transactions on Visualization and Computer Graphics symmetries visualization design visualization theory 2014 infovis14--2346265 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data. The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures-interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored. Dykes, J. Hauser, H. Slingsby, A. Turkay, C. Wood, J. geographic statistics InfoVis cities and towns geographic information systems spatial resolution statistics visual analytics IEEE Transactions on Visualization and Computer Graphics geographic information geovisualization interactive data analysis multi-variate data visual analytics 2014 infovis14--2346258 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Axis Calibration for Improving Data Attribute Estimation in Star Coordinates Plots. Star coordinates is a well-known multivariate visualization method that produces linear dimensionality reduction mappings through a set of radial axes defined by vectors in an observable space. One of its main drawbacks concerns the difficulty to recover attributes of data samples accurately, which typically lie in the [0], [1] interval, given the locations of the low-dimensional embeddings and the vectors. In this paper we show that centering the data can considerably increase attribute estimation accuracy, where data values can be read off approximately by projecting embedded points onto calibrated (i.e., labeled) axes, similarly to classical statistical biplots. In addition, this idea can be coupled with a recently developed orthonormalization process on the axis vectors that prevents unnecessary distortions. We demonstrate that the combination of both approaches not only enhances the estimates, but also provides more faithful representations of the data. Rubio-Sanchez, M. Sanchez, A. radial InfoVis calibration data visualization estimation error linear systems multivariate regression IEEE Transactions on Visualization and Computer Graphics attribute value estimation axis calibration biplots data centering orthographic projection radviz star coordinates 2014 infovis14--2346456 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time. With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes. Bhatele, A. Bremer, P.-T. Gamblin, T. Hamann, B. Isaacs, K.E. Jusufi, I. Schulz, M. cluster color history InfoVis data visualization image color analysis large-scale systems performance analysis supercomputers IEEE Transactions on Visualization and Computer Graphics information visualization performance analysis software visualization timelines traces 2014 infovis14--2346420 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Comparative Eye Tracking Study on Node-Link Visualizations of Trajectories. We present the results of an eye tracking study that compares different visualization methods for long, dense, complex, and piecewise linear spatial trajectories. Typical sources of such data are from temporally discrete measurements of the positions of moving objects, for example, recorded GPS tracks of animals in movement ecology. In the repeated-measures within-subjects user study, four variants of node-link visualization techniques are compared, with the following representations of directed links: standard arrow, tapered, equidistant arrows, and equidistant comets. In addition, we investigate the effect of rendering order for the halo visualization of those links as well as the usefulness of node splatting. All combinations of link visualization techniques are tested for different trajectory density levels. We used three types of tasks: tracing of paths, identification of longest links, and estimation of the density of trajectory clusters. Results are presented in the form of the statistical evaluation of task completion time, task solution accuracy, and two eye tracking metrics. These objective results are complemented by a summary of subjective feedback from the participants. The main result of our study is that tapered links perform very well. However, we discuss that equidistant comets and equidistant arrows are a good option to perceive direction information independent of zoom-level of the display. Burch, M. Netzel, R. Weiskopf, D. evaluation metrics user study zoom InfoVis data visualization encoding eyes rendering (computer graphics) sorting tracking trajectory IEEE Transactions on Visualization and Computer Graphics direction encoding evaluation eye tracking halo rendering node splatting node-link visualization trajectory visualization user study 2014 infovis14--2346292 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Constructing Visual Representations: Investigating the Use of Tangible Tokens. The accessibility of infovis authoring tools to a wide audience has been identified as a major research challenge. A key task in the authoring process is the development of visual mappings. While the infovis community has long been deeply interested in finding effective visual mappings, comparatively little attention has been placed on how people construct visual mappings. In this paper, we present the results of a study designed to shed light on how people transform data into visual representations. We asked people to create, update and explain their own information visualizations using only tangible building blocks. We learned that all participants, most of whom had little experience in visualization authoring, were readily able to create and talk about their own visualizations. Based on our observations, we discuss participants' actions during the development of their visual representations and during their analytic activities. We conclude by suggesting implications for tool design to enable broader support for infovis authoring. Carpendale, S. Huron, S. Jansen, Y. InfoVis authoring tools context awareness data visualization encoding image color analysis publishing IEEE Transactions on Visualization and Computer Graphics constructive visualization dynamic visualization empirical study information visualization novices physical visualization token visual analytics visual mapping visualization authoring visualization construction 2014 infovis14--2346331 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Design Activity Framework for Visualization Design. An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research. Agutter, J. Mazur, D. McKenna, S. Meyer, M. collaboration InfoVis data visualization design methodology encoding prototypes IEEE Transactions on Visualization and Computer Graphics cybersecurity decisions design evaluation frameworks models nested model process visualization 2014 infovis14--2346250 11/12/2014 IEEE Transactions on Visualization and Computer Graphics DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation. We introduce a new direct manipulation technique, DimpVis, for interacting with visual items in information visualizations to enable exploration of the time dimension. DimpVis is guided by visual hint paths which indicate how a selected data item changes through the time dimension in a visualization. Temporal navigation is controlled by manipulating any data item along its hint path. All other items are updated to reflect the new time. We demonstrate how the DimpVis technique can be designed to directly manipulate position, colour, and size in familiar visualizations such as bar charts and scatter plots, as a means for temporal navigation. We present results from a comparative evaluation, showing that the DimpVis technique was subjectively preferred and quantitatively competitive with the traditional time slider, and significantly faster than small multiples for a variety of tasks. Collins, C. Kondo, B. evaluation navigation small multiples InfoVis data visualization image color analysis market research motion segmentation time-varying systems trajectory IEEE Transactions on Visualization and Computer Graphics direct manipulation information visualization time navigation 2014 infovis14--2346260 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Domino: Extracting, Comparing, and Manipulating Subsets Across Multiple Tabular Datasets. Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. Gehlenborg, N. Gratzl, S. Lex, A. Pfister, H. Streit, M. InfoVis biomedical measurements cancer data visualization genomics IEEE Transactions on Visualization and Computer Graphics categorical data heterogeneous data multiple coordinated views relationships visual linking 2014 infovis14--2346437 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Effects of Presentation Mode and Pace Control on Performance in Image Classification. A common task in visualization is to quickly find interesting items in large sets. When appropriate metadata is missing, automatic queries are impossible and users have to inspect all elements visually. We compared two fundamentally different, but obvious display modes for this task and investigated the difference with respect to effectiveness, efficiency, and satisfaction. The static mode is based on the page metaphor and presents successive pages with a static grid of items. The moving mode is based on the conveyor belt metaphor and lets a grid of items slide though the screen in a continuous flow. In our evaluation, we applied both modes to the common task of browsing images. We performed two experiments where 18 participants had to search for certain target images in a large image collection. The number of shown images per second (pace) was predefined in the first experiment, and under user control in the second one. We conclude that at a fixed pace, the mode has no significant impact on the recall. The perceived pace is generally slower for moving mode, which causes users to systematically choose for a faster real pace than in static mode at the cost of recall, keeping the average number of target images found per second equal for both modes. van der Corput, P. van Wijk, J.J. evaluation experiment InfoVis analysis of variance classification image classification time factors usability IEEE Transactions on Visualization and Computer Graphics image browsing image classification multimedia visualization rsvp 2014 infovis14--2346298 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. When making an inference or comparison with uncertain, noisy, or incomplete data, measurement error and confidence intervals can be as important for judgment as the actual mean values of different groups. These often misunderstood statistical quantities are frequently represented by bar charts with error bars. This paper investigates drawbacks with this standard encoding, and considers a set of alternatives designed to more effectively communicate the implications of mean and error data to a general audience, drawing from lessons learned from the use of visual statistics in the information visualization community. We present a series of crowd-sourced experiments that confirm that the encoding of mean and error significantly changes how viewers make decisions about uncertain data. Careful consideration of design tradeoffs in the visual presentation of data results in human reasoning that is more consistently aligned with statistical inferences. We suggest the use of gradient plots (which use transparency to encode uncertainty) and violin plots (which use width) as better alternatives for inferential tasks than bar charts with error bars. Correll, M. Gleicher, M. statistics uncertainty InfoVis data visualization encoding error analysis information analysis standards IEEE Transactions on Visualization and Computer Graphics crowd-sourcing empirical evaluation information visualization visual statistics 2014 infovis14--2346435 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Exploring the Placement and Design of Word-Scale Visualizations. We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines-small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations. Fekete, J.-D. Goffin, P. Isenberg, P. Willett, W. document text InfoVis context awareness data visualization encoding text analysis text processing IEEE Transactions on Visualization and Computer Graphics design space glyphs information visualization sparklines text visualization word-scale visualizations 2014 infovis14--2346320 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Four Experiments on the Perception of Bar Charts. Bar charts are one of the most common visualization types. In a classic graphical perception paper, Cleveland & McGill studied how different bar chart designs impact the accuracy with which viewers can complete simple perceptual tasks. They found that people perform substantially worse on stacked bar charts than on aligned bar charts, and that comparisons between adjacent bars are more accurate than between widely separated bars. However, the study did not explore why these differences occur. In this paper, we describe a series of follow-up experiments to further explore and explain their results. While our results generally confirm Cleveland & McGill's ranking of various bar chart configurations, we provide additional insight into the bar chart reading task and the sources of participants' errors. We use our results to propose new hypotheses on the perception of bar charts. Anand, A. Setlur, V. Talbot, J. insight perception InfoVis bar charts data visualization estimation information analysis three-dimensional displays visual analytics IEEE Transactions on Visualization and Computer Graphics bar charts graphical perception 2014 infovis14--2346444 11/12/2014 IEEE Transactions on Visualization and Computer Graphics GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration. The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs. Foerster, F. Goel, A. Chau, D.H. Kahng, M. Lin, Z. Stasko, J. Stolper, C.D. graph network InfoVis aggregates data visualization graph theory interactive systems semantics IEEE Transactions on Visualization and Computer Graphics graph analysis graph visualization graph-level operations information visualization visualization technique specification 2014 infovis14--2346433 11/12/2014 IEEE Transactions on Visualization and Computer Graphics How Hierarchical Topics Evolve in Large Text Corpora. Using a sequence of topic trees to organize documents is a popular way to represent hierarchical and evolving topics in text corpora. However, following evolving topics in the context of topic trees remains difficult for users. To address this issue, we present an interactive visual text analysis approach to allow users to progressively explore and analyze the complex evolutionary patterns of hierarchical topics. The key idea behind our approach is to exploit a tree cut to approximate each tree and allow users to interactively modify the tree cuts based on their interests. In particular, we propose an incremental evolutionary tree cut algorithm with the goal of balancing 1) the fitness of each tree cut and the smoothness between adjacent tree cuts; 2) the historical and new information related to user interests. A time-based visualization is designed to illustrate the evolving topics over time. To preserve the mental map, we develop a stable layout algorithm. As a result, our approach can quickly guide users to progressively gain profound insights into evolving hierarchical topics. We evaluate the effectiveness of the proposed method on Amazon's Mechanical Turk and real-world news data. The results show that users are able to successfully analyze evolving topics in text data. Cui, W. Liu, S. Wei, H. Wu, Z. text InfoVis algorithm design and analysis context awareness data visualization document handling text analysis text mining IEEE Transactions on Visualization and Computer Graphics data transformation evolutionary tree clustering hierarchical topic visualization 2014 infovis14--2346291 11/12/2014 IEEE Transactions on Visualization and Computer Graphics iVisDesigner: Expressive Interactive Design of Information Visualizations. We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, exemplify it through a variety of illustrative visualization designs and discuss its limitations. A performance analysis and an informal user study are presented to evaluate the system. Hollerer, T. Ren, D. Yuan, X. brushing evaluation graph user study InfoVis data visualization information analysis programming profession web and internet services IEEE Transactions on Visualization and Computer Graphics expressiveness interaction interactive design visualization design web-based visualization 2014 infovis14--2346978 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Learning Perceptual Kernels for Visualization Design. Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowd-sourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types-including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement-and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions. Bernstein, M.S. Demiralp, C.D. Heer, J. color evaluation ordinal perception InfoVis color analysis data visualization encoding image color analysis kernel shape analysis visualization IEEE Transactions on Visualization and Computer Graphics automated visualization crowdsourcing design encoding model perception visual embedding visualization 2014 infovis14--2346454 11/12/2014 IEEE Transactions on Visualization and Computer Graphics LiveGantt: Interactively Visualizing a Large Manufacturing Schedule. In this paper, we introduce LiveGantt as a novel interactive schedule visualization tool that helps users explore highly-concurrent large schedules from various perspectives. Although a Gantt chart is the most common approach to illustrate schedules, currently available Gantt chart visualization tools suffer from limited scalability and lack of interactions. LiveGantt is built with newly designed algorithms and interactions to improve conventional charts with better scalability, explorability, and reschedulability. It employs resource reordering and task aggregation to display the schedules in a scalable way. LiveGantt provides four coordinated views and filtering techniques to help users explore and interact with the schedules in more flexible ways. In addition, LiveGantt is equipped with an efficient rescheduler to allow users to instantaneously modify their schedules based on their scheduling experience in the fields. To assess the usefulness of the application of LiveGantt, we conducted a case study on manufacturing schedule data with four industrial engineering researchers. Participants not only grasped an overview of a schedule but also explored the schedule from multiple perspectives to make enhancements. Huh, J. Jo, J. Kim, B. Park, J. Seo, J. case study coordinated views overview InfoVis filtering interactive systems job shop scheduling production facilities scheduling IEEE Transactions on Visualization and Computer Graphics event sequence visualization exploratory interactions schedule visualization simplification simulation 2014 infovis14--2346311 11/12/2014 IEEE Transactions on Visualization and Computer Graphics MovExp: A Versatile Visualization Tool for Human-Computer Interaction Studies with 3D Performance and Biomechanical Data. In Human-Computer Interaction (HCI), experts seek to evaluate and compare the performance and ergonomics of user interfaces. Recently, a novel cost-efficient method for estimating physical ergonomics and performance has been introduced to HCI. It is based on optical motion capture and biomechanical simulation. It provides a rich source for analyzing human movements summarized in a multidimensional data set. Existing visualization tools do not sufficiently support the HCI experts in analyzing this data. We identified two shortcomings. First, appropriate visual encodings are missing particularly for the biomechanical aspects of the data. Second, the physical setup of the user interface cannot be incorporated explicitly into existing tools. We present MovExp, a versatile visualization tool that supports the evaluation of user interfaces. In particular, it can be easily adapted by the HCI experts to include the physical setup that is being evaluated, and visualize the data on top of it. Furthermore, it provides a variety of visual encodings to communicate muscular loads, movement directions, and other specifics of HCI studies that employ motion capture and biomechanical simulation. In this design study, we follow a problem-driven research approach. Based on a formalization of the visualization needs and the data structure, we formulate technical requirements for the visualization tool and present novel solutions to the analysis needs of the HCI experts. We show the utility of our tool with four case studies from the daily work of our HCI experts. Bachynskyi, M. Oulasvirta, A. Palmas, G. Seidel, H.-P. Weinkauf, T. design study evaluation interaction InfoVis biological system modeling biomechanics data visualization ergonomics human computer interaction IEEE Transactions on Visualization and Computer Graphics design study human-computer interaction information visualization 2014 infovis14--2346323 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization. We reflect on a four-year engagement with transport authorities and others involving a large dataset describing the use of a public bicycle-sharing scheme. We describe the role visualization of these data played in fostering engagement with policy makers, transport operators, the transport research community, the museum and gallery sector and the general public. We identify each of these as `channels'-evolving relationships between producers and consumers of visualization-where traditional roles of the visualization expert and domain expert are blurred. In each case, we identify the different design decisions that were required to support each of these channels and the role played by the visualization process. Using chauffeured interaction with a flexible visual analytics system we demonstrate how insight was gained by policy makers into gendered spatio-temporal cycle behaviors, how this led to further insight into workplace commuting activity, group cycling behavior and explanations for street navigation choice. We demonstrate how this supported, and was supported by, the seemingly unrelated development of narrative-driven visualization via TEDx, of the creation and the setting of an art installation and the curating of digital and physical artefacts. We assert that existing models of visualization design, of tool/technique development and of insight generation do not adequately capture the richness of parallel engagement via these multiple channels of communication. We argue that developing multiple channels in parallel opens up opportunities for visualization design and analysis by building trust and authority and supporting creativity. This rich, non-sequential approach to visualization design is likely to foster serendipity, deepen insight and increase impact. Beecham, R. Dykes, J. Wood, J. insight interaction navigation visual analytics InfoVis computer interfaces data visualization datasets sequential analysis visual analytics IEEE Transactions on Visualization and Computer Graphics bikeshare design study impact movement visualization visual analytics visualization models 2014 infovis14--2346441 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations. Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free. van den Elzen, S. van Wijk, J.J. financial network overview InfoVis clutter context awareness data visualization image color analysis network topology IEEE Transactions on Visualization and Computer Graphics direct manipulation interaction multivariate networks selections of interest 2014 infovis14--2346312 11/12/2014 IEEE Transactions on Visualization and Computer Graphics NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity. We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data. Al-Awami, A.K. Beyer, J. Hadwiger, M. Kasthuri, N. Lichtman, J.W. Pfister, H. Strobelt, H. InfoVis data visualization nanoscale devices navigation nerve fibers neurophysiology scalability three-dimensional displays IEEE Transactions on Visualization and Computer Graphics connectomics data abstraction focus+context multi-trees neuroscience 2014 infovis14--2346276 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Nmap: A Novel Neighborhood Preservation Space-filling Algorithm. Space-filling techniques seek to use as much as possible the visual space to represent a dataset, splitting it into regions that represent the data elements. Amongst those techniques, Treemaps have received wide attention due to its simplicity, reduced visual complexity, and compact use of the available space. Several different Treemap algorithms have been proposed, however the core idea is the same, to divide the visual space into rectangles with areas proportional to some data attribute or weight. Although pleasant layouts can be effectively produced by the existing techniques, most of them do not take into account relationships that might exist between different data elements when partitioning the visual space. This violates the distance-similarity metaphor, that is, close rectangles do not necessarily represent similar data elements. In this paper, we propose a novel approach, called Neighborhood Treemap (Nmap), that seeks to solve this limitation by employing a slice and scale strategy where the visual space is successively bisected on the horizontal or vertical directions and the bisections are scaled until one rectangle is defined per data element. Compared to the current techniques with the same similarity preservation goal, our approach presents the best results while being two to three orders of magnitude faster. The usefulness of Nmap is shown by two applications involving the organization of document collections and the construction of cartograms illustrating its effectiveness on different scenarios. Duarte, F.S.L.G. Fadel, S.G. Fatore, F.M. Paulovich, F.V. Sikansi, F. document treemap InfoVis algorithm design and analysis cartography image color analysis shape analysis terrain mapping IEEE Transactions on Visualization and Computer Graphics distance-similarity preservation space-filling techniques treemaps 2014 infovis14--2346422 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation. Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools. Borner, K. Kobourov, S. Saket, B. Simonetto, P. evaluation experiment graph network InfoVis data visualization datasets diagrams image color analysis layout visualization IEEE Transactions on Visualization and Computer Graphics graphs maps networks scatter plots 2014 infovis14--2346249 11/12/2014 IEEE Transactions on Visualization and Computer Graphics OnSet: A Visualization Technique for Large-scale Binary Set Data. Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data. The visualization technique defines a single, combined domain of elements for all sets, and models each set by the elements that it both contains and does not contain. OnSet employs direct manipulation interaction and visual highlighting to support easy identification of commonalities and differences as well as membership patterns across different sets of elements. We present case studies to illustrate how the technique can be successfully applied across different domains such as bio-chemical metabolomics and task and event scheduling. Dove, A. Major, T. Sadana, R. Stasko, J. interaction InfoVis complexity theory data visualization image color analysis large-scale systems nonhomogeneous media IEEE Transactions on Visualization and Computer Graphics direct manipulation euler diagrams information visualization interaction logical operations set visualization 2014 infovis14--2346428 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Order of Magnitude Markers: An Empirical Study on Large Magnitude Number Detection. In this paper we introduce Order of Magnitude Markers (OOMMs) as a new technique for number representation. The motivation for this work is that many data sets require the depiction and comparison of numbers that have varying orders of magnitude. Existing techniques for representation use bar charts, plots and colour on linear or logarithmic scales. These all suffer from related problems. There is a limit to the dynamic range available for plotting numbers, and so the required dynamic range of the plot can exceed that of the depiction method. When that occurs, resolving, comparing and relating values across the display becomes problematical or even impossible for the user. With this in mind, we present an empirical study in which we compare logarithmic, linear, scale-stack bars and our new markers for 11 different stimuli grouped into 4 different tasks across all 8 marker types. Borgo, R. Dearden, J. Jones, M.W. InfoVis datasets numerical models order of magnitude markers IEEE Transactions on Visualization and Computer Graphics bar charts logarithmic scale orders of magnitude 2014 infovis14--2346271 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Origin-Destination Flow Data Smoothing and Mapping. This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data. Guo, D. Zhu, X. geographic perception InfoVis bandwidth allocation data visualization feature extraction flow graphs smoothing methods statistics IEEE Transactions on Visualization and Computer Graphics flow mapping generalization graph drawing kernel smoothing multi-resolution mapping spatial data mining 2014 infovis14--2346431 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists. For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system üin the wildý, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of üexploringý a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology. Brehmer, M. Ingram, S. Munzner, T. Stray, J. clustering document interaction overview text InfoVis data visualization document handling encoding text analysis text mining IEEE Transactions on Visualization and Computer Graphics design study investigative journalism task and requirements analysis text analysis text and document data 2014 infovis14--2346293 11/12/2014 IEEE Transactions on Visualization and Computer Graphics PanoramicData: Data Analysis through Pen & Touch. Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data). Existing approaches either target specific different problem domains (e.g., data-transformation or data-presentation) or expose only limited aspects of the general exploratory process; in either case, users are forced to adopt coping strategies (e.g., arranging windows or using undo as a mechanism for comparison instead of using side-by-side displays) to compensate for the lack of an integrated suite of exploratory tools. PanoramicData (PD) addresses these problems by unifying a comprehensive set of tools for visual data exploration into a hybrid pen and touch system designed to exploit the visualization advantages of large interactive displays. PD goes beyond just familiar visualizations by including direct UI support for data transformation and aggregation, filtering and brushing. Leveraging an unbounded whiteboard metaphor, users can combine these tools like building blocks to create detailed interactive visual display networks in which each visualization can act as a filter for others. Further, by operating directly on relational-databases, PD provides an approachable visual language that exposes a broad set of the expressive power of SQL including functionally complete logic filtering, computation of aggregates and natural table joins. To understand the implications of this novel approach, we conducted a formative user study with both data and visualization experts. The results indicated that the system provided a fluid and natural user experience for probing multi-dimensional data and was able to cover the full range of queries that the users wanted to pose. Drucker, S.M. Zeleznik, R. Zgraggen, E. brushing filter user study InfoVis data analysis data visualization filtering image color analysis multidimensional systems relational databases IEEE Transactions on Visualization and Computer Graphics coordinated and multiple views interaction design pen and touch user interfaces visual analytics 2014 infovis14--2346979 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Ranking Visualizations of Correlation Using Weber's Law. Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n = 1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization. Chang, R. Franconeri, S. Harrison, L. Yang, F. experiment perception InfoVis crowdsourcing data models data visualization design methodology image color analysis testing IEEE Transactions on Visualization and Computer Graphics evaluation perception visualization 2014 infovis14--2346998 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Reinforcing Visual Grouping Cues to Communicate Complex Informational Structure. In his book Multimedia Learning [7], Richard Mayer asserts that viewers learn best from imagery that provides them with cues to help them organize new information into the correct knowledge structures. Designers have long been exploiting the Gestalt laws of visual grouping to deliver viewers those cues using visual hierarchy, often communicating structures much more complex than the simple organizations studied in psychological research. Unfortunately, designers are largely practical in their work, and have not paused to build a complex theory of structural communication. If we are to build a tool to help novices create effective and well structured visuals, we need a better understanding of how to create them. Our work takes a first step toward addressing this lack, studying how five of the many grouping cues (proximity, color similarity, common region, connectivity, and alignment) can be effectively combined to communicate structured text and imagery from real world examples. To measure the effectiveness of this structural communication, we applied a digital version of card sorting, a method widely used in anthropology and cognitive science to extract cognitive structures. We then used tree edit distance to measure the difference between perceived and communicated structures. Our most significant findings are: 1) with careful design, complex structure can be communicated clearly; 2) communicating complex structure is best done with multiple reinforcing grouping cues; 3) common region (use of containers such as boxes) is particularly effective at communicating structure; and 4) alignment is a weak structural communicator. Bae, J. Watson, B. color hierarchy text theory InfoVis color analysis image color analysis information analysis multimedia communication psychology visual communication IEEE Transactions on Visualization and Computer Graphics gestalt principles perception visual communication visual grouping visual hierarchy 2014 infovis14--2346279 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Revisiting Bertin Matrices: New Interactions for Crafting Tabular Visualizations. We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to üsimplify without destroyingý by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.COMPUTER Dragicevic, P. Fekete, J.-D. Perin, C. interaction matrix InfoVis data visualization encoding tabular measurements visual analytics IEEE Transactions on Visualization and Computer Graphics bertin crossets crossing interaction tabular data visualization 2014 infovis14--2346274 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Stenomaps: Shorthand for shapes. We address some of the challenges in representing spatial data with a novel form of geometric abstraction-the stenomap. The stenomap comprises a series of smoothly curving linear glyphs that each represent both the boundary and the area of a polygon. We present an efficient algorithm to automatically generate these open, C1-continuous splines from a set of input polygons. Feature points of the input polygons are detected using the medial axis to maintain important shape properties. We use dynamic programming to compute a planar non-intersecting spline representing each polygon's base shape. The results are stylised glyphs whose appearance may be parameterised and that offer new possibilities in the 'cartographic design space'. We compare our glyphs with existing forms of geometric schematisation and discuss their relative merits and shortcomings. We describe several use cases including the depiction of uncertain model data in the form of hurricane track forecasting; minimal ink thematic mapping; and the depiction of continuous statistical data. Reimer, A. Speckmann, B. van Goethem, A. Wood, J. InfoVis algorithm design and analysis complexity theory data visualization dynamic programming feature extraction shape analysis splines (mathematics) IEEE Transactions on Visualization and Computer Graphics algorithm design maps schematisation 2014 infovis14--2346445 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TenniVis: Visualization for Tennis Match Analysis. Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos. Hu, Y. Polk, T. Yang, J. Zhao, Y. usability InfoVis cameras data visualization entertainment games image color analysis information analysis IEEE Transactions on Visualization and Computer Graphics sports analytics tennis visualization visual knowledge discovery 2014 infovis14--2346452 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Effects of Interactive Latency on Exploratory Visual Analysis. To support effective exploration, it is often stated that interactive visualizations should provide rapid response times. However, the effects of interactive latency on the process and outcomes of exploratory visual analysis have not been systematically studied. We present an experiment measuring user behavior and knowledge discovery with interactive visualizations under varying latency conditions. We observe that an additional delay of 500ms incurs significant costs, decreasing user activity and data set coverage. Analyzing verbal data from think-aloud protocols, we find that increased latency reduces the rate at which users make observations, draw generalizations and generate hypotheses. Moreover, we note interaction effects in which initial exposure to higher latencies leads to subsequently reduced performance in a low-latency setting. Overall, increased latency causes users to shift exploration strategy, in turn affecting performance. We discuss how these results can inform the design of interactive analysis tools. Heer, J. Liu, Z. experiment interaction InfoVis data visualization image color analysis interactive services visual analytics visualization IEEE Transactions on Visualization and Computer Graphics exploratory analysis interaction interactive visualization latency scalability user performance verbal analysis 2014 infovis14--2346426 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Influence of Contour on Similarity Perception of Star Glyphs. We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph-enhanced through contour lines-would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e. g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyp- s. Bertini, E. Bezerianos, A. Fischer, F. Fuchs, J. Isenberg, P. experiment glyph perception InfoVis active contours data analysis data visualization shape analysis IEEE Transactions on Visualization and Computer Graphics contours glyphs perception quantitative evaluation similarity detection star glyphs visual comparison 2014 infovis14--2346424 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Not-so-Staggering Effect of Staggered Animated Transitions on Visual Tracking. Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research. Chevalier, F. Dragicevic, P. Franconeri, S. animation occlusion InfoVis animation complexity theory data visualization psychology target tracking tracking IEEE Transactions on Visualization and Computer Graphics animated transitions staggered animation visual tracking 2014 infovis14--2346419 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Persuasive Power of Data Visualization. Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization. Bertini, E. Manivannan, A. Nov, O. Pandey, A.V. Satterthwaite, M. InfoVis data visualization games market research performance evaluation psychology user interfaces IEEE Transactions on Visualization and Computer Graphics elaboration likelihood model evaluation persuasive visualization 2014 infovis14--2346983 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The relation between visualization size, grouping, and user performance. In this paper we make the following contributions: (1) we describe how the grouping, quantity, and size of visual marks affects search time based on the results from two experiments; (2) we report how search performance relates to self-reported difficulty in finding the target for different display types; and (3) we present design guidelines based on our findings to facilitate the design of effective visualizations. Both Experiment 1 and 2 asked participants to search for a unique target in colored visualizations to test how the grouping, quantity, and size of marks affects user performance. In Experiment 1, the target square was embedded in a grid of squares and in Experiment 2 the target was a point in a scatterplot. Search performance was faster when colors were spatially grouped than when they were randomly arranged. The quantity of marks had little effect on search time for grouped displays (üpop-outý), but increasing the quantity of marks slowed reaction time for random displays. Regardless of color layout (grouped vs. random), response times were slowest for the smallest mark size and decreased as mark size increased to a point, after which response times plateaued. In addition to these two experiments we also include potential application areas, as well as results from a small case study where we report preliminary findings that size may affect how users infer how visualizations should be used. We conclude with a list of design guidelines that focus on how to best create visualizations based on grouping, quantity, and size of visual marks. Gramazio, C.C. Laidlaw, D.H. Schloss, K.B. case study color experiment scatterplot InfoVis data visualization image color analysis layout monitoring time factors visualization IEEE Transactions on Visualization and Computer Graphics graphical perception information visualization layout size 2014 infovis14--2346277 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Tree Colors: Color Schemes for Tree-Structured Data. We present a method to map tree structures to colors from the Hue-Chroma-Luminance color model, which is known for its well balanced perceptual properties. The Tree Colors method can be tuned with several parameters, whose effect on the resulting color schemes is discussed in detail. We provide a free and open source implementation with sensible parameter defaults. Categorical data are very common in statistical graphics, and often these categories form a classification tree. We evaluate applying Tree Colors to tree structured data with a survey on a large group of users from a national statistical institute. Our user study suggests that Tree Colors are useful, not only for improving node-link diagrams, but also for unveiling tree structure in non-hierarchical visualizations. de Jonge, E. Tennekes, M. categorical color user study InfoVis color analysis image color analysis terrain mapping trees IEEE Transactions on Visualization and Computer Graphics color schemes hierarchical data statistical graphics 2014 infovis14--2346248 11/12/2014 IEEE Transactions on Visualization and Computer Graphics UpSet: Visualization of Intersecting Sets. Understanding relationships between sets is an important analysis task that has received widespread attention in the visualization community. The major challenge in this context is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. In this paper we introduce UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections. UpSet is focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersections, and a duality between the visualization of the elements in a dataset and their set membership. UpSet visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. Sorting according to various measures enables a task-driven analysis of relevant intersections and aggregates. The elements represented in the sets and their associated attributes are visualized in a separate view. Queries based on containment in specific intersections, aggregates or driven by attribute filters are propagated between both views. We also introduce several advanced visual encodings and interaction methods to overcome the problems of varying scales and to address scalability. UpSet is web-based and open source. We demonstrate its general utility in multiple use cases from various domains. Gehlenborg, N. Lex, A. Pfister, H. Strobelt, H. Vuillemot, R. interaction matrix statistics InfoVis data visualization information analysis power generation sorting visualization IEEE Transactions on Visualization and Computer Graphics multidimensional data set attributes set relationships set visualization sets sets intersections 2014 infovis14--2346321 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Parameter Space Analysis: A Conceptual Framework. Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes. Bruckner, S. Heinzl, C. Möller, T. Piringer, H. Sedlmair, M. evaluation navigation InfoVis analytical models biological system modeling computational modeling data models image segmentation predictive models IEEE Transactions on Visualization and Computer Graphics input-output model literature analysis parameter space analysis simulation task characterization 2014 infovis14--2346297 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visualizing Statistical Mix Effects and Simpson's Paradox. We discuss how ümix effectsý can surprise users of visualizations and potentially lead them to incorrect conclusions. This statistical issue (also known as üomitted variable biasý or, in extreme cases, as üSimpson's paradoxý) is widespread and can affect any visualization in which the quantity of interest is an aggregated value such as a weighted sum or average. Our first contribution is to document how mix effects can be a serious issue for visualizations, and we analyze how mix effects can cause problems in a variety of popular visualization techniques, from bar charts to treemaps. Our second contribution is a new technique, the ücomet chart,ý that is meant to ameliorate some of these issues. Armstrong, Z. Wattenberg, M. document InfoVis data visualization image color analysis image segmentation statistics IEEE Transactions on Visualization and Computer Graphics mix effects omitted variable bias simpson's paradox statistics 2014 infovis15--2467191 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. General visualization tools typically require manual specification of views: analysts must select data variables and then choose which transformations and visual encodings to apply. These decisions often involve both domain and visualization design expertise, and may impose a tedious specification process that impedes exploration. In this paper, we seek to complement manual chart construction with interactive navigation of a gallery of automatically-generated visualizations. We contribute Voyager, a mixed-initiative system that supports faceted browsing of recommended charts chosen according to statistical and perceptual measures. We describe Voyager's architecture, motivating design principles, and methods for generating and interacting with visualization recommendations. In a study comparing Voyager to a manual visualization specification tool, we find that Voyager facilitates exploration of previously unseen data and leads to increased data variable coverage. We then distill design implications for visualization tools, in particular the need to balance rapid exploration and targeted question-answering. Anand, A. Heer, J. Howe, B. Mackinlay, J.D. Moritz, D. Wongsuphasawat, K. navigation InfoVis browsers compass data visualization encoding grammar image color analysis visualization IEEE Transactions on Visualization and Computer Graphics exploratory analysis information visualization mixed-initiative systems user interfaces visualization recommendation 2015 infovis15--2467754 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visually Comparing Weather Features in Forecasts. Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data. Meyer, M. Quinan, P.S. design study InfoVis color data visualization encoding image color analysis predictive models weather forecasting IEEE Transactions on Visualization and Computer Graphics design study ensemble data geographic/geospatial visualization weather 2015 infovis15--2467199 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visualizing Multiple Variables Across Scale and Geography. Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains - geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable. Dykes, J. Goodwin, S. Slingsby, A. Turkay, C. geographic statistics InfoVis context correlation geography input variables prototypes spatial resolution visualization IEEE Transactions on Visualization and Computer Graphics energy geodemographics geography local statistics multivariate scale sensitivity analysis variable selection 2015 infovis15--2467112 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visualization, Selection, and Analysis of Traffic Flows. Visualization of the trajectories of moving objects leads to dense and cluttered images, which hinders exploration and understanding. It also hinders adding additional visual information, such as direction, and makes it difficult to interactively extract traffic flows, i.e., subsets of trajectories. In this paper we present our approach to visualize traffic flows and provide interaction tools to support their exploration. We show an overview of the traffic using a density map. The directions of traffic flows are visualized using a particle system on top of the density map. The user can extract traffic flows using a novel selection widget that allows for the intuitive selection of an area, and filtering on a range of directions and any additional attributes. Using simple, visual set expressions, the user can construct more complicated selections. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We validate our approach through use cases where we explore and analyze the temporal behavior of aircraft and vessel trajectories, e.g., landing and takeoff sequences, or the evolution of flight route density. The aircraft use cases have been developed and validated in collaboration with domain experts. Hurter, C. Scheepens, R. van de Wetering, H. van Wijk, J.J. collaboration interaction overview InfoVis aircraft color data mining data visualization image color analysis trajectory visualization IEEE Transactions on Visualization and Computer Graphics interaction moving object visualization traffic flows 2015 infovis15--2467831 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Mementos: Reflecting Memories with Personal Data. In this paper we discuss the creation of visual mementos as a new application area for visualization. We define visual mementos as visualizations of personally relevant data for the purpose of reminiscing, and sharing of life experiences. Today more people collect digital information about their life than ever before. The shift from physical to digital archives poses new challenges and opportunities for self-reflection and self-representation. Drawing on research on autobiographical memory and on the role of artifacts in reminiscing, we identified design challenges for visual mementos: mapping data to evoke familiarity, expressing subjectivity, and obscuring sensitive details for sharing. Visual mementos can make use of the known strengths of visualization in revealing patterns to show the familiar instead of the unexpected, and extend representational mappings beyond the objective to include the more subjective. To understand whether people's subjective views on their past can be reflected in a visual representation, we developed, deployed and studied a technology probe that exemplifies our concept of visual mementos. Our results show how reminiscing has been supported and reveal promising new directions for self-reflection and sharing through visual mementos of personal experiences. Baur, D. Carpendale, S. Huron, S. Thudt, A. InfoVis data privacy data visualization global positioning system history loading privacy visualization IEEE Transactions on Visualization and Computer Graphics memories movement data personal visualization visual memento world wide web 2015 infovis15--2467752 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Encodings of Temporal Uncertainty: A Comparative User Study. A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error bars, and ambiguation. Our results reveal significant differences in error rates and completion time for these different visualization types and different tasks. We recommend using ambiguation - using a lighter color value to represent uncertain regions - or error bars for judging durations and temporal bounds, and gradient plots - using fading color or transparency - for judging probability values. BoĚ�gl, M. Federico, P. Gschwandtner, T. Miksch, S. color uncertainty user study InfoVis bars data visualization encoding image color analysis uncertainty visualization IEEE Transactions on Visualization and Computer Graphics temporal intervals uncertainty visualization 2015 infovis15--2467911 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Vials: Visualizing Alternative Splicing of Genes. Alternative splicing is a process by which the same DNA sequence is used to assemble different proteins, called protein isoforms. Alternative splicing works by selectively omitting some of the coding regions (exons) typically associated with a gene. Detection of alternative splicing is difficult and uses a combination of advanced data acquisition methods and statistical inference. Knowledge about the abundance of isoforms is important for understanding both normal processes and diseases and to eventually improve treatment through targeted therapies. The data, however, is complex and current visualizations for isoforms are neither perceptually efficient nor scalable. To remedy this, we developed Vials, a novel visual analysis tool that enables analysts to explore the various datasets that scientists use to make judgments about isoforms: the abundance of reads associated with the coding regions of the gene, evidence for junctions, i.e., edges connecting the coding regions, and predictions of isoform frequencies. Vials is scalable as it allows for the simultaneous analysis of many samples in multiple groups. Our tool thus enables experts to (a) identify patterns of isoform abundance in groups of samples and (b) evaluate the quality of the data. We demonstrate the value of our tool in case studies using publicly available datasets. Alsallakh, B. Borowsky, M. Botros, J. Lex, A. Peterson, B. Pfister, H. Strobelt, H. InfoVis bioinformatics data visualization dna encoding genomics junctions splicing IEEE Transactions on Visualization and Computer Graphics biology visualization directed acyclic graphs mrna-seq multivariate networks protein isoforms 2015 infovis15--2467325 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients. We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan. Carpendale, S. Hill, M. Kamal, N. Loorak, M.H. Perin, C. collaboration matrix InfoVis computed tomography data visualization delays hospitals interviews needles visualization IEEE Transactions on Visualization and Computer Graphics electronic health records multi-dimensional data multi-dimensional data, temporal event sequences 2015 infovis15--2467751 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data. Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. One-dimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. In this paper, we comparatively evaluate existing methods, such as Stack Zoom [15] and ChronoLenses [38], giving a graphical overview of each and classifying their ability to explore and interact with data. We propose new visualizations and other extensions to the existing approaches. We undertake and report an empirical study and a field study using these techniques. Borgo, R. Jones, M.W. Walker, J. field study interaction overview zoom InfoVis context data mining data visualization layout lenses rivers visualization IEEE Transactions on Visualization and Computer Graphics focus+context interaction techniques lens time-series exploration 2015 infovis15--2467851 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data. We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets. Bach, B. Dragicevic, P. Grabowski, T. Heulot, N. Madhyastha, T. Shi, C. document network InfoVis data visualization electronic publishing encyclopedias history internet visualization IEEE Transactions on Visualization and Computer Graphics information visualization multidimensional scaling temporal data visualization 2015 infovis15--2467201 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Suggested Interactivity: Seeking Perceived Affordances for Information Visualization. In this article, we investigate methods for suggesting the interactivity of online visualizations embedded with text. We first assess the need for such methods by conducting three initial experiments on Amazon's Mechanical Turk. We then present a design space for Suggested Interactivity (i. e., visual cues used as perceived affordances-SI), based on a survey of 382 HTML5 and visualization websites. Finally, we assess the effectiveness of three SI cues we designed for suggesting the interactivity of bar charts embedded with text. Our results show that only one cue (SI3) was successful in inciting participants to interact with the visualizations, and we hypothesize this is because this particular cue provided feedforward. Boy, J. Detienne, F. Eveillard, L. Fekete, J.-D. text InfoVis brushes electronic publishing encyclopedias internet silicon visualization IEEE Transactions on Visualization and Computer Graphics information visualization for the people online visualization perceived affordances suggested interactivity 2015 infovis15--2467452 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Speculative Practices: Utilizing InfoVis to Explore Untapped Literary Collections. In this paper we exemplify how information visualization supports speculative thinking, hypotheses testing, and preliminary interpretation processes as part of literary research. While InfoVis has become a buzz topic in the digital humanities, skepticism remains about how effectively it integrates into and expands on traditional humanities research approaches. From an InfoVis perspective, we lack case studies that show the specific design challenges that make literary studies and humanities research at large a unique application area for information visualization. We examine these questions through our case study of the Speculative W@nderverse, a visualization tool that was designed to enable the analysis and exploration of an untapped literary collection consisting of thousands of science fiction short stories. We present the results of two empirical studies that involved general-interest readers and literary scholars who used the evolving visualization prototype as part of their research for over a year. Our findings suggest a design space for visualizing literary collections that is defined by (1) their academic and public relevance, (2) the tension between qualitative vs. quantitative methods of interpretation, (3) result-vs. process-driven approaches to InfoVis, and (4) the unique material and visual qualities of cultural collections. Through the Speculative W@nderverse we demonstrate how visualization can bridge these sometimes contradictory perspectives by cultivating curiosity and providing entry points into literary collections while, at the same time, supporting multiple aspects of humanities research processes. Forlini, S. Hinrichs, U. Moynihan, B. case study InfoVis context cultural differences data visualization fans metadata statistical analysis visualization IEEE Transactions on Visualization and Computer Graphics cultural collections digital humanities interlinked visualization literary studies science fiction 2015 infovis15--2469125 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Spatial Reasoning and Data Displays. Graphics convey numerical information very efficiently, but rely on a different set of mental processes than tabular displays. Here, we present a study relating demographic characteristics and visual skills to perception of graphical lineups. We conclude that lineups are essentially a classification test in a visual domain, and that performance on the lineup protocol is associated with general aptitude, rather than specific tasks such as card rotation and spatial manipulation. We also examine the possibility that specific graphical tasks may be associated with certain visual skills and conclude that more research is necessary to understand which visual skills are required in order to understand certain plot types. Hofmann, H. VanderPlas, S. perception InfoVis atmospheric measurements cognition particle measurements protocols sociology visualization IEEE Transactions on Visualization and Computer Graphics data visualization data visualization, perception statistical computing statistical graphics 2015 infovis15--2467271 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Sketching Designs Using the Five Design-Sheet Methodology. Sketching designs has been shown to be a useful way of planning and considering alternative solutions. The use of lo-fidelity prototyping, especially paper-based sketching, can save time, money and converge to better solutions more quickly. However, this design process is often viewed to be too informal. Consequently users do not know how to manage their thoughts and ideas (to first think divergently, to then finally converge on a suitable solution). We present the Five Design Sheet (FdS) methodology. The methodology enables users to create information visualization interfaces through lo-fidelity methods. Users sketch and plan their ideas, helping them express different possibilities, think through these ideas to consider their potential effectiveness as solutions to the task (sheet 1); they create three principle designs (sheets 2,3 and 4); before converging on a final realization design that can then be implemented (sheet 5). In this article, we present (i) a review of the use of sketching as a planning method for visualization and the benefits of sketching, (ii) a detailed description of the Five Design Sheet (FdS) methodology, and (iii) an evaluation of the FdS using the System Usability Scale, along with a case-study of its use in industry and experience of its use in teaching. Headleand, C. Ritsos, P.D. Roberts, J.C. evaluation usability InfoVis companies computer science data visualization planning prototypes visualization IEEE Transactions on Visualization and Computer Graphics c1140z c1160 c4210l c4240c ideation lo-fidelity prototyping sketching for visualization user-centred design 2015 infovis15--2467035 11/12/2014 IEEE Transactions on Visualization and Computer Graphics SchemeLens: A Content-Aware Vector-Based Fisheye Technique for Navigating Large Systems Diagrams. System schematics, such as those used for electrical or hydraulic systems, can be large and complex. Fisheye techniques can help navigate such large documents by maintaining the context around a focus region, but the distortion introduced by traditional fisheye techniques can impair the readability of the diagram. We present SchemeLens, a vector-based, topology-aware fisheye technique which aims to maintain the readability of the diagram. Vector-based scaling reduces distortion to components, but distorts layout. We present several strategies to reduce this distortion by using the structure of the topology, including orthogonality and alignment, and a model of user intention to foster smooth and predictable navigation. We evaluate this approach through two user studies: Results show that (1) SchemeLens is 16-27% faster than both round and rectangular flat-top fisheye lenses at finding and identifying a target along one or several paths in a network diagram; (2) augmenting SchemeLens with a model of user intentions aids in learning the network topology. Bailly, G. CoheĚ�, A. Eagan, J. Lecolinet, E. Liutkus, B. distortion fisheye navigation network InfoVis context distortion layout lenses navigation shape visualization IEEE Transactions on Visualization and Computer Graphics content-aware fisheye information visualization interactive zoom navigation network schematics vector-scaling 2015 infovis15--2467091 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization. We present Reactive Vega, a system architecture that provides the first robust and comprehensive treatment of declarative visual and interaction design for data visualization. Starting from a single declarative specification, Reactive Vega constructs a dataflow graph in which input data, scene graph elements, and interaction events are all treated as first-class streaming data sources. To support expressive interactive visualizations that may involve time-varying scalar, relational, or hierarchical data, Reactive Vega's dataflow graph can dynamically re-write itself at runtime by extending or pruning branches in a data-driven fashion. We discuss both compile- and run-time optimizations applied within Reactive Vega, and share the results of benchmark studies that indicate superior interactive performance to both D3 and the original, non-reactive Vega system. Heer, J. Hoffswell, J. Russell, R. Satyanarayan, A. graph interaction InfoVis computer architecture data models data visualization encoding indexes runtime visualization IEEE Transactions on Visualization and Computer Graphics declarative specification information visualization interaction optimization streaming data systems toolkits 2015 infovis15--2467717 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality Reductions. We introduce a set of integrated interaction techniques to interpret and interrogate dimensionality-reduced data. Projection techniques generally aim to make a high-dimensional information space visible in form of a planar layout. However, the meaning of the resulting data projections can be hard to grasp. It is seldom clear why elements are placed far apart or close together and the inevitable approximation errors of any projection technique are not exposed to the viewer. Previous research on dimensionality reduction focuses on the efficient generation of data projections, interactive customisation of the model, and comparison of different projection techniques. There has been only little research on how the visualization resulting from data projection is interacted with. We contribute the concept of probing as an integrated approach to interpreting the meaning and quality of visualizations and propose a set of interactive methods to examine dimensionality-reduced data as well as the projection itself. The methods let viewers see approximation errors, question the positioning of elements, compare them to each other, and visualize the influence of data dimensions on the projection space. We created a web-based system implementing these methods, and report on findings from an evaluation with data analysts using the prototype to examine multidimensional datasets. DoĚ�rk, M. MuĚ�ller, B. Stahnke, J. Thom, A. evaluation interaction InfoVis approximation error data visualization distortion heating prototypes stress visualization IEEE Transactions on Visualization and Computer Graphics dimensionality reduction information visualization interactivity multidimensional scaling 2015 infovis15--2467811 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Poemage: Visualizing the Sonic Topology of a Poem. The digital humanities have experienced tremendous growth within the last decade, mostly in the context of developing computational tools that support what is called distant reading - collecting and analyzing huge amounts of textual data for synoptic evaluation. On the other end of the spectrum is a practice at the heart of the traditional humanities, close reading - the careful, in-depth analysis of a single text in order to extract, engage, and even generate as much productive meaning as possible. The true value of computation to close reading is still very much an open question. During a two-year design study, we explored this question with several poetry scholars, focusing on an investigation of sound and linguistic devices in poetry. The contributions of our design study include a problem characterization and data abstraction of the use of sound in poetry as well as Poemage, a visualization tool for interactively exploring the sonic topology of a poem. The design of Poemage is grounded in the evaluation of a series of technology probes we deployed to our poetry collaborators, and we validate the final design with several case studies that illustrate the disruptive impact technology can have on poetry scholarship. Finally, we also contribute a reflection on the challenges we faced conducting visualization research in literary studies. Coles, K. Lein, J. McCurdy, N. Meyer, M. design study evaluation text InfoVis context data visualization pragmatics probes stress topology visualization IEEE Transactions on Visualization and Computer Graphics design studies graph/network data text and document data visualization in the humanities 2015 infovis15--2467872 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Orientation-Enhanced Parallel Coordinate Plots. Parallel Coordinate Plots (PCPs) is one of the most powerful techniques for the visualization of multivariate data. However, for large datasets, the representation suffers from clutter due to overplotting. In this case, discerning the underlying data information and selecting specific interesting patterns can become difficult. We propose a new and simple technique to improve the display of PCPs by emphasizing the underlying data structure. Our Orientation-enhanced Parallel Coordinate Plots (OPCPs) improve pattern and outlier discernibility by visually enhancing parts of each PCP polyline with respect to its slope. This enhancement also allows us to introduce a novel and efficient selection method, the Orientation-enhanced Brushing (O-Brushing). Our solution is particularly useful when multiple patterns are present or when the view on certain patterns is obstructed by noise. We present the results of our approach with several synthetic and real-world datasets. Finally, we conducted a user evaluation, which verifies the advantages of the OPCPs in terms of discernibility of information in complex data. It also confirms that O-Brushing eases the selection of data patterns in PCPs and reduces the amount of necessary user interactions compared to state-of-the-art brushing techniques. Breeuwer, M. Eisemann, E. Eisemann, M. Raidou, R.G. Vilanova, A. brushing evaluation InfoVis brushes clutter data structures data visualization histograms kernel visualization IEEE Transactions on Visualization and Computer Graphics brushing data readability data selection orientation-enhanced brushing orientation-enhanced parallel coordinates parallel coordinates 2015 infovis15--2467132 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Optimal Sets of Projections of High-Dimensional Data. Finding good projections of n-dimensional datasets into a 2D visualization domain is one of the most important problems in Information Visualization. Users are interested in getting maximal insight into the data by exploring a minimal number of projections. However, if the number is too small or improper projections are used, then important data patterns might be overlooked. We propose a data-driven approach to find minimal sets of projections that uniquely show certain data patterns. For this we introduce a dissimilarity measure of data projections that discards affine transformations of projections and prevents repetitions of the same data patterns. Based on this, we provide complete data tours of at most n/2 projections. Furthermore, we propose optimal paths of projection matrices for an interactive data exploration. We illustrate our technique with a set of state-of-the-art real high-dimensional benchmark datasets. Lehmann, D.J. Theisel, H. high-dimensional data insight InfoVis benchmark testing convergence data visualization iris measurement principal component analysis visualization IEEE Transactions on Visualization and Computer Graphics high-dimensional data multivariate projections radial visualization star coordinates 2015 infovis15--2467322 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators. A composite indicator (CI) is a measuring and benchmark tool used to capture multi-dimensional concepts, such as Information and Communication Technology (ICT) usage. Individual indicators are selected and combined to reflect a phenomena being measured. Visualization of a composite indicator is recommended as a tool to enable interested stakeholders, as well as the public audience, to better understand the indicator components and evolution overtime. However, existing CI visualizations introduce a variety of solutions and there is a lack in CI's visualization guidelines. Radial visualizations are popular among these solutions because of CI's inherent multi-dimensionality. Although in dispute, Radar-charts are often used for CI presentation. However, no empirical evidence on Radar's effectiveness and efficiency for common CI tasks is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares the Radar chart technique with two other radial visualization methods: Flowercharts as used in the well-known OECD Betterlife index, and Circle-charts which could be adopted for this purpose. Examples of these charts in the current context are shown in Figure 1. We evaluated these charts, showing the same data with each of the mentioned techniques applying small multiple views for different dimensions of the data. We compared users' performance and preference empirically under a formal task-taxonomy. Results indicate that the Radar chart was the least effective and least liked, while performance of the two other options were mixed and dependent on the task. Results also showed strong preference of participants toward the Flower chart. Summarizing our results, we provide specific design guidelines for composite indicator visualization. Albo, Y. Bak, P. Lanir, J. Rafaeli, S. evaluation experiment multiple views radial taxonomy InfoVis benchmark testing cities and towns data visualization image color analysis indexes radar visualization IEEE Transactions on Visualization and Computer Graphics composite indicator visualization experiment radial layout design visualization evaluation 2015 infovis15--2466971 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Matches, Mismatches, and Methods: Multiple-View Workflows for Energy Portfolio Analysis. The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base. Brehmer, M. Munzner, T. Ng, J. Tate, K. design study InfoVis aggregates buildings data visualization encoding portfolios time series analysis visualization IEEE Transactions on Visualization and Computer Graphics coordinated and multiple views design methodologies design study task and requirements analysis time series data 2015 infovis15--2467758 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability. Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields. Afergan, D. Chang, R. Han, P.K.J. Harrison, L. Ottley, A. Peck, E.M. Taylor, H.A. Ziemkiewicz, C. text InfoVis accuracy bayes methods breast cancer cognition diseases sociology visualization IEEE Transactions on Visualization and Computer Graphics bayesian reasoning individual differences spatial ability visualization 2015 infovis15--2467195 11/12/2014 IEEE Transactions on Visualization and Computer Graphics How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking. In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information VIsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations. Hung, Y. Kang, Y. Kim, S.-H. Lam, H. Lee, S. Yi, J.S. sensemaking theory treemap InfoVis data visualization encoding hidden markov models image color analysis interviews vehicles visualization IEEE Transactions on Visualization and Computer Graphics grounded theory information visualization novice users qualitative study sensemaking model 2015 infovis15--2467451 11/12/2014 IEEE Transactions on Visualization and Computer Graphics HOLA: Human-like Orthogonal Network Layout. Over the last 50 years a wide variety of automatic network layout algorithms have been developed. Some are fast heuristic techniques suitable for networks with hundreds of thousands of nodes while others are multi-stage frameworks for higher-quality layout of smaller networks. However, despite decades of research currently no algorithm produces layout of comparable quality to that of a human. We give a new “human-centred” methodology for automatic network layout algorithm design that is intended to overcome this deficiency. User studies are first used to identify the aesthetic criteria algorithms should encode, then an algorithm is developed that is informed by these criteria and finally, a follow-up study evaluates the algorithm output. We have used this new methodology to develop an automatic orthogonal network layout method, HOLA, that achieves measurably better (by user study) layout than the best available orthogonal layout algorithm and which produces layouts of comparable quality to those produced by hand. Dwyer, T. Kieffer, S. Marriott, K. Wybrow, M. network user study InfoVis algorithm design and analysis layout manuals software software algorithms standards visualization IEEE Transactions on Visualization and Computer Graphics automatic layout algorithms graph layout graph-drawing aesthetics orthogonal layout user-generated layout 2015 infovis15--2467251 11/12/2014 IEEE Transactions on Visualization and Computer Graphics High-Quality Ultra-Compact Grid Layout of Grouped Networks. Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks. Dwyer, T. Gange, G. Kieffer, S. Klein, K. Marriott, K. Yoghourdjian, V. graph network InfoVis containers encoding layout optimization pipelines routing standards IEEE Transactions on Visualization and Computer Graphics graph drawing large-neighborhood search network visualization optimization power graph 2015 infovis15--2467759 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Guidelines for Effective Usage of Text Highlighting Techniques. Semi-automatic text analysis involves manual inspection of text. Often, different text annotations (like part-of-speech or named entities) are indicated by using distinctive text highlighting techniques. In typesetting there exist well-known formatting conventions, such as bold typeface, italics, or background coloring, that are useful for highlighting certain parts of a given text. Also, many advanced techniques for visualization and highlighting of text exist; yet, standard typesetting is common, and the effects of standard typesetting on the perception of text are not fully understood. As such, we surveyed and tested the effectiveness of common text highlighting techniques, both individually and in combination, to discover how to maximize pop-out effects while minimizing visual interference between techniques. To validate our findings, we conducted a series of crowd-sourced experiments to determine: i) a ranking of nine commonly-used text highlighting techniques; ii) the degree of visual interference between pairs of text highlighting techniques; iii) the effectiveness of techniques for visual conjunctive search. Our results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques. We discuss the pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers. Chul, B. Oelke, D. Pfister, H. Schreck, T. Strobelt, H. perception text InfoVis color data visualization image color analysis interference natural language processing text analysis visualization IEEE Transactions on Visualization and Computer Graphics crowdsourced study text annotation text highlighting techniques visual document analytics 2015 infovis15--2466992 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research. The parallel coordinates technique is widely used for the analysis of multivariate data. During recent decades significant research efforts have been devoted to exploring the applicability of the technique and to expand upon it, resulting in a variety of extensions. Of these many research activities, a surprisingly small number concerns user-centred evaluations investigating actual use and usability issues for different tasks, data and domains. The result is a clear lack of convincing evidence to support and guide uptake by users as well as future research directions. To address these issues this paper contributes a thorough literature survey of what has been done in the area of user-centred evaluation of parallel coordinates. These evaluations are divided into four categories based on characterization of use, derived from the survey. Based on the data from the survey and the categorization combined with the authors' experience of working with parallel coordinates, a set of guidelines for future research directions is proposed. Forsell, C. Johansson, J. evaluation overview parallel coordinates usability InfoVis clutter correlation guidelines layout standards three-dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics evaluation guidelines parallel coordinates survey 2015 infovis15--2467671 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Beyond Weber's Law: A Second Look at Ranking Visualizations of Correlation. Models of human perception - including perceptual “laws” - can be valuable tools for deriving visualization design recommendations. However, it is important to assess the explanatory power of such models when using them to inform design. We present a secondary analysis of data previously used to rank the effectiveness of bivariate visualizations for assessing correlation (measured with Pearson's r) according to the well-known Weber-Fechner Law. Beginning with the model of Harrison et al. [1], we present a sequence of refinements including incorporation of individual differences, log transformation, censored regression, and adoption of Bayesian statistics. Our model incorporates all observations dropped from the original analysis, including data near ceilings caused by the data collection process and entire visualizations dropped due to large numbers of observations worse than chance. This model deviates from Weber's Law, but provides improved predictive accuracy and generalization. Using Bayesian credibility intervals, we derive a partial ranking that groups visualizations with similar performance, and we give precise estimates of the difference in performance between these groups. We find that compared to other visualizations, scatterplots are unique in combining low variance between individuals and high precision on both positively- and negatively correlated data. We conclude with a discussion of the value of data sharing and replication, and share implications for modeling similar experimental data. Heer, J. Kay, M. perception statistics InfoVis analytical models correlation data models data visualization gaussian distribution predictive models visualization IEEE Transactions on Visualization and Computer Graphics bayesian methods censored regression log transformation perception of correlation weber's law weber’s law 2015 infovis15--2467732 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Beyond Memorability: Visualization Recognition and Recall. In this paper we move beyond memorability and investigate how visualizations are recognized and recalled. For this study we labeled a dataset of 393 visualizations and analyzed the eye movements of 33 participants as well as thousands of participant-generated text descriptions of the visualizations. This allowed us to determine what components of a visualization attract people's attention, and what information is encoded into memory. Our findings quantitatively support many conventional qualitative design guidelines, including that (1) titles and supporting text should convey the message of a visualization, (2) if used appropriately, pictograms do not interfere with understanding and can improve recognition, and (3) redundancy helps effectively communicate the message. Importantly, we show that visualizations memorable “at-a-glance” are also capable of effectively conveying the message of the visualization. Thus, a memorable visualization is often also an effective one. Bainbridge, C.M. Borkin, D. Borkin, M. Bylinskii, Z. Oliva, A. Pfister, H. Wook, N. Yeh, C.S. text InfoVis atmospheric measurements data visualization encoding particle measurements redundancy target recognition visualization IEEE Transactions on Visualization and Computer Graphics eye-tracking study information visualization memorability recall recognition 2015 infovis15--2467323 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Automatic Selection of Partitioning Variables for Small Multiple Displays. Effective small multiple displays are created by partitioning a visualization on variables that reveal interesting conditional structure in the data. We propose a method that automatically ranks partitioning variables, allowing analysts to focus on the most promising small multiple displays. Our approach is based on a randomized, non-parametric permutation test, which allows us to handle a wide range of quality measures for visual patterns defined on many different visualization types, while discounting spurious patterns. We demonstrate the effectiveness of our approach on scatterplots of real-world, multidimensional datasets. Anand, A. Talbot, J. InfoVis algorithm design and analysis data visualization employment histograms partitioning algorithms visual analytics IEEE Transactions on Visualization and Computer Graphics multidimensional data small multiple displays visualization selection 2015 infovis15--2467691 11/12/2014 IEEE Transactions on Visualization and Computer Graphics AmbiguityVis: Visualization of Ambiguity in Graph Layouts. Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graph layout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteria simultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attention to these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visual ambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguity in edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in community structures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others are then displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualization approaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a user to explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectiveness of the technique is demonstrated through case studies and expert reviews. Archambault, D. Qu, H. Shen, Q. Wang, Y. Yang, S. Zhou, Z. Zhu, M. graph graph drawing graph layout metrics network InfoVis entropy heating image edge detection layout measurement readability metrics visualization IEEE Transactions on Visualization and Computer Graphics graph layout graph visualization node-link diagram visual ambiguity visualization 2015 infovis15--2467051 11/12/2014 IEEE Transactions on Visualization and Computer Graphics AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations. Datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elements, sets, and the set intersections. Increasing the number of sets results in a combinatorial growth of relations and creates scalability challenges. Exploratory tasks (e.g. selection, comparison) have commonly been designed in separation for set-typed attributes, which reduces interface consistency. To improve on scalability and to support rich, contextual exploration of set-typed data, we present AggreSet. AggreSet creates aggregations for each data dimension: sets, set-degrees, set-pair intersections, and other attributes. It visualizes the element count per aggregate using a matrix plot for set-pair intersections, and histograms for set lists, set-degrees and other attributes. Its non-overlapping visual design is scalable to numerous and large sets. AggreSet supports selection, filtering, and comparison as core exploratory tasks. It allows analysis of set relations inluding subsets, disjoint sets and set intersection strength, and also features perceptual set ordering for detecting patterns in set matrices. Its interaction is designed for rich and rapid data exploration. We demonstrate results on a wide range of datasets from different domains with varying characteristics, and report on expert reviews and a case study using student enrollment and degree data with assistant deans at a major public university. Bederson, B.B. Elmqvist, N. Yalcin, M.A. case study interaction matrix InfoVis aggregates chapters data visualization filtering motion pictures scalability visualization IEEE Transactions on Visualization and Computer Graphics data exploration design interaction multi-valued attributes scalability set visualization sets visualization 2015 infovis15--2467321 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives. While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design. Angus, D. Byrne, L. Wiles, J. social InfoVis context data visualization encoding image color analysis shape visualization IEEE Transactions on Visualization and Computer Graphics design methodologies illustrative visualization taxonomies visual design 2015 infovis15--2467992 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Simple Approach for Boundary Improvement of Euler Diagrams. General methods for drawing Euler diagrams tend to generate irregular polygons. Yet, empirical evidence indicates that smoother contours make these diagrams easier to read. In this paper, we present a simple method to smooth the boundaries of any Euler diagram drawing. When refining the diagram, the method must ensure that set elements remain inside their appropriate boundaries and that no region is removed or created in the diagram. Our approach uses a force system that improves the diagram while at the same time ensuring its topological structure does not change. We demonstrate the effectiveness of the approach through case studies and quantitative evaluations. Archambault, D. Scheidegger, C. Simonetto, P. InfoVis complexity theory extremities force junctions shape smoothing methods visualization IEEE Transactions on Visualization and Computer Graphics boundary improvement euler diagrams force-directed approaches 2015 infovis15--2467951 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Psychophysical Investigation of Size as a Physical Variable. Physical visualizations, or data physicalizations, encode data in attributes of physical shapes. Despite a considerable body of work on visual variables, “physical variables” remain poorly understood. One of them is physical size. A difficulty for solid elements is that “size” is ambiguous - it can refer to either length/diameter, surface, or volume. Thus, it is unclear for designers of physicalizations how to effectively encode quantities in physical size. To investigate, we ran an experiment where participants estimated ratios between quantities represented by solid bars and spheres. Our results suggest that solid bars are compared based on their length, consistent with previous findings for 2D and 3D bars on flat media. But for spheres, participants' estimates are rather proportional to their surface. Depending on the estimation method used, judgments are rather consistent across participants, thus the use of perceptually-optimized size scales seems possible. We conclude by discussing implications for the design of data physicalizations and the need for more empirical studies on physical variables. Hornbæk, K. Jansen, Y. experiment InfoVis brushes clutter data structures data visualization histograms kernel visualization IEEE Transactions on Visualization and Computer Graphics data physicalization experiment physical variable physical visualization psychophysics 2015 infovis15--2467471 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Linguistic Approach to Categorical Color Assignment for Data Visualization. When data categories have strong color associations, it is useful to use these semantically meaningful concept-color associations in data visualizations. In this paper, we explore how linguistic information about the terms defining the data can be used to generate semantically meaningful colors. To do this effectively, we need first to establish that a term has a strong semantic color association, then discover which color or colors express it. Using co-occurrence measures of color name frequencies from Google n-grams, we define a measure for colorability that describes how strongly associated a given term is to any of a set of basic color terms. We then show how this colorability score can be used with additional semantic analysis to rank and retrieve a representative color from Google Images. Alternatively, we use symbolic relationships defined by WordNet to select identity colors for categories such as countries or brands. To create visually distinct color palettes, we use k-means clustering to create visually distinct sets, iteratively reassigning terms with multiple basic color associations as needed. This can be additionally constrained to use colors only in a predefined palette. Setlur, V. Stone, M.C. categorical clustering color InfoVis color context data visualization databases google image color analysis semantics IEEE Transactions on Visualization and Computer Graphics categorical color color names google n-grams linguistics natural language processing semantics wordnet xkcd 2015 infovis15--2467324 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A comparative study between RadViz and Star Coordinates. RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks. Diaz, F. Raya, L. Rubio-Sanchez, M. Sanchez, A. radial InfoVis data analysis data visualization distributed databases layout nonlinear distortion shape springs IEEE Transactions on Visualization and Computer Graphics classication classification cluster analysis exploratory data analysis outlier detection radviz star coordinates 2015 infovis16--2598589 10/25/2016 IEEE Transactions on Visualization and Computer Graphics WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making. A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions. Möller, T. Pajer, S. Piringer, H. Spechtenhauser, F. Streit, M. Torsney-Weir, T. awareness evaluation filter uncertainty InfoVis additives automobiles context decision making sensitivity space exploration visualization IEEE Transactions on Visualization and Computer Graphics decision making interactive ranking multi-objective optimization rank sensitivity visual analysis 2016 infovis16--2598590 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visualizing Social Media Content with SentenTree. We introduce SentenTree, a novel technique for visualizing the content of unstructured social media text. SentenTree displays frequent sentence patterns abstracted from a corpus of social media posts. The technique employs design ideas from word clouds and the Word Tree, but overcomes a number of limitations of both those visualizations. SentenTree displays a node-link diagram where nodes are words and links indicate word co-occurrence within the same sentence. The spatial arrangement of nodes gives cues to the syntactic ordering of words while the size of nodes gives cues to their frequency of occurrence. SentenTree can help people gain a rapid understanding of key concepts and opinions in a large social media text collection. It is implemented as a lightweight application that runs in the browser. Hu, M. Stasko, J. Wongsuphasawat, K. social text InfoVis context games layout media tag clouds twitter visualization IEEE Transactions on Visualization and Computer Graphics natural language processing social media text visualization twitter word cloud 2016 infovis16--2598839 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration. Although data visualization tools continue to improve, during the data exploration process many of them require users to manually specify visualization techniques, mappings, and parameters. In response, we present the Visualization by Demonstration paradigm, a novel interaction method for visual data exploration. A system which adopts this paradigm allows users to provide visual demonstrations of incremental changes to the visual representation. The system then recommends potential transformations (Visual Representation, Data Mapping, Axes, and View Specification transformations) from the given demonstrations. The user and the system continue to collaborate, incrementally producing more demonstrations and refining the transformations, until the most effective possible visualization is created. As a proof of concept, we present VisExemplar, a mixed-initiative prototype that allows users to explore their data by recommending appropriate transformations in response to the given demonstrations. Brown, E.T. Endert, A. Kim, H. Saket, B. interaction InfoVis automobiles bars data visualization encoding image color analysis spatial databases visualization IEEE Transactions on Visualization and Computer Graphics visual data exploration visualization by demonstration visualization tools 2016 infovis16--2598592 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visplause: Visual Data Quality Assessment of Many Time Series Using Plausibility Checks. Trends like decentralized energy production lead to an exploding number of time series from sensors and other sources that need to be assessed regarding their data quality (DQ). While the identification of DQ problems for such routinely collected data is typically based on existing automated plausibility checks, an efficient inspection and validation of check results for hundreds or thousands of time series is challenging. The main contribution of this paper is the validated design of Visplause, a system to support an efficient inspection of DQ problems for many time series. The key idea of Visplause is to utilize meta-information concerning the semantics of both the time series and the plausibility checks for structuring and summarizing results of DQ checks in a flexible way. Linked views enable users to inspect anomalies in detail and to generate hypotheses about possible causes. The design of Visplause was guided by goals derived from a comprehensive task analysis with domain experts in the energy sector. We reflect on the design process by discussing design decisions at four stages and we identify lessons learned. We also report feedback from domain experts after using Visplause for a period of one month. This feedback suggests significant efficiency gains for DQ assessment, increased confidence in the DQ, and the applicability of Visplause to summarize indicators also outside the context of DQ. Arbesser, C. MĂĽhlbacher, T. Piringer, H. Spechtenhauser, F. time series InfoVis data models inspection photovoltaic systems production sensors time series analysis IEEE Transactions on Visualization and Computer Graphics data quality assessment hierarchical aggregation high-dimensional data linked views 2016 infovis16--2599030 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Vega-Lite: A Grammar of Interactive Graphics. We present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization. In contrast to existing reactive specifications, Vega-Lite selections decompose an interaction design into concise, enumerable semantic units. We evaluate Vega-Lite through a range of examples, demonstrating succinct specification of both customized interaction methods and common techniques such as panning, zooming, and linked selection. Heer, J. Moritz, D. Satyanarayan, A. Wongsuphasawat, K. interaction zooming InfoVis brushes data visualization encoding grammar transforms visualization IEEE Transactions on Visualization and Computer Graphics declarative specification information visualization interaction systems toolkits 2016 infovis16--2599338 10/25/2016 IEEE Transactions on Visualization and Computer Graphics VizItCards: A Card-Based Toolkit for Infovis Design Education. Shifts in information visualization practice are forcing a reconsideration of how infovis is taught. Traditional curricula that focused on conveying research-derived knowledge are slowly integrating design thinking as a key learning objective. In part, this is motivated by the realization that infovis is a wicked design problem, requiring a different kind of design work. In this paper we describe, VizItCards, a card-driven workshop developed for our graduate infovis class. The workshop is intended to provide practice with good design techniques and to simultaneously reinforce key concepts. VizItCards relies on principles of collaborative-learning and research on parallel design to generate positive collaborations and high-quality designs. From our experience of simulating a realistic design scenario in a classroom setting, we find that our students were able to meet key learning objectives and their design performance improved during the class. We describe variants of the workshop, discussing which techniques we think match to which learning goals. Adar, E. He, S. education toolkit InfoVis collaboration conferences data visualization education human computer interaction standards visualization IEEE Transactions on Visualization and Computer Graphics card design workshop information visualization education peer learning toolkit 2016 infovis16--2598958 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Towards Unambiguous Edge Bundling: Investigating Confluent Drawings for Network Visualization. In this paper, we investigate Confluent Drawings (CD), a technique for bundling edges in node-link diagrams based on network connectivity. Edge-bundling techniques are designed to reduce edge clutter in node-link diagrams by coalescing lines into common paths or bundles. Unfortunately, traditional bundling techniques introduce ambiguity since edges are only bundled by spatial proximity, rather than network connectivity; following an edge from its source to its target can lead to the perception of incorrect connectivity if edges are not clearly separated within the bundles. Contrary, CDs bundle edges based on common sources or targets. Thus, a smooth path along a confluent bundle indicates precise connectivity. While CDs have been described in theory, practical investigation and application to real-world networks (i.e., networks beyond those with certain planarity restrictions) is currently lacking. Here, we provide the first algorithm for constructing CDs from arbitrary directed and undirected networks and present a simple layout method, embedded in a sand box environment providing techniques for interactive exploration. We then investigate patterns and artifacts in CDs, which we compare to other common edge-bundling techniques. Finally, we present the first user study that compares edge-compression techniques, including CD, power graphs, metro-style, and common edge bundling. We found that users without particular expertise in visualization or network analysis are able to read small CDs without difficulty. Compared to existing bundling techniques, CDs are more likely to allow people to correctly perceive connectivity. Bach, B. Dwyer, T. Hurter, C. Marriott, K. Henry Riche, N. network perception theory user study InfoVis australia clutter complex networks layout systematics topology visualization IEEE Transactions on Visualization and Computer Graphics bundling confluent edge compression network visualization power graph 2016 infovis16--2598594 10/25/2016 IEEE Transactions on Visualization and Computer Graphics The Attraction Effect in Information Visualization. The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualizations. Since visualizations can be used to support decision making - e.g., when choosing a house to buy or an employee to hire - a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect. Bezerianos, A. Dimara, E. Dragicevic, P. experiment scatterplot text InfoVis cognition companies data visualization decision making education uncertainty visualization IEEE Transactions on Visualization and Computer Graphics asymmetric dominance effect attraction effect cognitive bias decision-making decoy effect information visualization 2016 infovis16--2598876 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement. Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets. Bryan, C. Ma, K.-L. Woodring, J. awareness graph InfoVis additives context data analysis data visualization layout strips visualization IEEE Transactions on Visualization and Computer Graphics annotations comic strip visualization narrative visualization storytelling time-varying data 2016 infovis16--2598618 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Surprise! Bayesian Weighting for De-Biasing Thematic Maps. Thematic maps are commonly used for visualizing the density of events in spatial data. However, these maps can mislead by giving visual prominence to known base rates (such as population densities) or to artifacts of sample size and normalization (such as outliers arising from smaller, and thus more variable, samples). In this work, we adapt Bayesian surprise to generate maps that counter these biases. Bayesian surprise, which has shown promise for modeling human visual attention, weights information with respect to how it updates beliefs over a space of models. We introduce Surprise Maps, a visualization technique that weights event data relative to a set of spatia-temporal models. Unexpected events (those that induce large changes in belief over the model space) are visualized more prominently than those that follow expected patterns. Using both synthetic and real-world datasets, we demonstrate how Surprise Maps overcome some limitations of traditional event maps. Correll, M. Heer, J. InfoVis bayes methods data models data visualization sociology spatial databases statistics visualization IEEE Transactions on Visualization and Computer Graphics bayesian surprise event visualization spatia-temporal data thematic maps 2016 infovis16--2598542 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Small Multiples with Gaps. Small multiples enable comparison by providing different views of a single data set in a dense and aligned manner. A common frame defines each view, which varies based upon values of a conditioning variable. An increasingly popular use of this technique is to project two-dimensional locations into a gridded space (e.g. grid maps), using the underlying distribution both as the conditioning variable and to determine the grid layout. Using whitespace in this layout has the potential to carry information, especially in a geographic context. Yet, the effects of doing so on the spatial properties of the original units are not understood. We explore the design space offered by such small multiples with gaps. We do so by constructing a comprehensive suite of metrics that capture properties of the layout used to arrange the small multiples for comparison (e.g. compactness and alignment) and the preservation of the original data (e.g. distance, topology and shape). We study these metrics in geographic data sets with varying properties and numbers of gaps. We use simulated annealing to optimize for each metric and measure the effects on the others. To explore these effects systematically, we take a new approach, developing a system to visualize this design space using a set of interactive matrices. We find that adding small amounts of whitespace to small multiple arrays improves some of the characteristics of 2D layouts, such as shape, distance and direction. This comes at the cost of other metrics, such as the retention of topology. Effects vary according to the input maps, with degree of variation in size of input regions found to be a factor. Optima exist for particular metrics in many cases, but at different amounts of whitespace for different maps. We suggest multiple metrics be used in optimized layouts, finding topology to be a primary factor in existing manually-crafted solutions, followed by a trade-off between shape and displacement. But the rich range of possible optimized layouts leads us to challenge single-solution thinking; we suggest to consider alternative optimized layouts for small multiples with gaps. Key to our work is the systematic, quantified and visual approach to exploring design spaces when facing a trade-off between many competing criteria-an approach likely to be of value to the analysis of other design spaces. Dykes, J. Meulemans, W. Slingsby, A. Turkay, C. Wood, J. geographic metrics small multiples InfoVis layout measurement shape space exploration topology two dimensional displays IEEE Transactions on Visualization and Computer Graphics design space geographic visualization metrics optimization small multiples whitespace 2016 infovis16--2598587 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Screenit: Visual Analysis of Cellular Screens. High-throughput and high-content screening enables large scale, cost-effective experiments in which cell cultures are exposed to a wide spectrum of drugs. The resulting multivariate data sets have a large but shallow hierarchical structure. The deepest level of this structure describes cells in terms of numeric features that are derived from image data. The subsequent level describes enveloping cell cultures in terms of imposed experiment conditions (exposure to drugs). We present Screenit, a visual analysis approach designed in close collaboration with screening experts. Screenit enables the navigation and analysis of multivariate data at multiple hierarchy levels and at multiple levels of detail. Screenit integrates the interactive modeling of cell physical states (phenotypes) and the effects of drugs on cell cultures (hits). In addition, quality control is enabled via the detection of anomalies that indicate low-quality data, while providing an interface that is designed to match workflows of screening experts. We demonstrate analyses for a real-world data set, CellMorph, with 6 million cells across 20,000 cell cultures. Borowsky, M. Dinkla, K. Genest, B. Pfister, H. Reiling, S. Strobelt, H. collaboration experiment hierarchy navigation InfoVis biological cells cells (biology) drugs multivariate data sets quality control IEEE Transactions on Visualization and Computer Graphics biology feature selection hierarchy high-content screening image classification multivariate visual analysis 2016 infovis16--2598541 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps. One critical visual task when using choropleth maps is to identify spatial clusters in the data. If spatial units have the same color and are in the same neighborhood, this region can be visually identified as a spatial cluster. However, the choice of classification method used to create the choropleth map determines the visual output. The critical map elements in the classification scheme are those that lie near the classification boundary as those elements could potentially belong to different classes with a slight adjustment of the classification boundary. Thus, these elements have the most potential to impact the visual features (i.e., spatial clusters) that occur in the choropleth map. We present a methodology to enable analysts and designers to identify spatial regions where the visual appearance may be the result of spurious data artifacts. The proposed methodology automatically detects the critical boundary cases that can impact the overall visual presentation of the choropleth map using a classification metric of cluster stability. The map elements that belong to a critical boundary case are then automatically assessed to quantify the visual impact of classification edge effects. Our results demonstrate the impact of boundary elements on the resulting visualization and suggest that special attention should be given to these elements during map design. Maciejewski, R. Zhang, Y. cluster color InfoVis correlation data mining data visualization image color analysis measurement spatial databases visualization IEEE Transactions on Visualization and Computer Graphics choropleth classification geodemographics geovisualization visualization 2016 infovis16--2598919 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Probabilistic Graph Layout for Uncertain Network Visualization. We present a novel uncertain network visualization technique based on node-link diagrams. Nodes expand spatially in our probabilistic graph layout, depending on the underlying probability distributions of edges. The visualization is created by computing a two-dimensional graph embedding that combines samples from the probabilistic graph. A Monte Carlo process is used to decompose a probabilistic graph into its possible instances and to continue with our graph layout technique. Splatting and edge bundling are used to visualize point clouds and network topology. The results provide insights into probability distributions for the entire network-not only for individual nodes and edges. We validate our approach using three data sets that represent a wide range of network types: synthetic data, protein-protein interactions from the STRING database, and travel times extracted from Google Maps. Our approach reveals general limitations of the force-directed layout and allows the user to recognize that some nodes of the graph are at a specific position just by chance. Brandes, U. Deussen, O. Goertler, J. Nocaj, A. Schulz, C. Weiskopf, D. database graph graph layout network InfoVis data visualization layout probabilistic logic probability density function probability distribution uncertainty visualization IEEE Transactions on Visualization and Computer Graphics edge bundling graph layout graph visualization monte carlo method uncertainty visualization 2016 infovis16--2598588 10/25/2016 IEEE Transactions on Visualization and Computer Graphics PROACT: Iterative Design of a Patient-Centered Visualization for Effective Prostate Cancer Health Risk Communication. Prostate cancer is the most common cancer among men in the US, and yet most cases represent localized cancer for which the optimal treatment is unclear. Accumulating evidence suggests that the available treatment options, including surgery and conservative treatment, result in a similar prognosis for most men with localized prostate cancer. However, approximately 90% of patients choose surgery over conservative treatment, despite the risk of severe side effects like erectile dysfunction and incontinence. Recent medical research suggests that a key reason is the lack of patient-centered tools that can effectively communicate personalized risk information and enable them to make better health decisions. In this paper, we report the iterative design process and results of developing the PROgnosis Assessment for Conservative Treatment (PROACT) tool, a personalized health risk communication tool for localized prostate cancer patients. PROACT utilizes two published clinical prediction models to communicate the patients' personalized risk estimates and compare treatment options. In collaboration with the Maine Medical Center, we conducted two rounds of evaluations with prostate cancer survivors and urologists to identify the design elements and narrative structure that effectively facilitate patient comprehension under emotional distress. Our results indicate that visualization can be an effective means to communicate complex risk information to patients with low numeracy and visual literacy. However, the visualizations need to be carefully chosen to balance readability with ease of comprehension. In addition, due to patients' charged emotional state, an intuitive narrative structure that considers the patients' information need is critical to aid the patients' comprehension of their risk information. Chang, R. Gutheil, C. Hakone, A. Han, P.K.J. Harrison, L. Ottley, A. Winters, N. collaboration InfoVis data visualization medical diagnostic imaging prognostics and health management prostate cancer visualization IEEE Transactions on Visualization and Computer Graphics and dissemination design studies medical visualization presentation production task and requirement analysis 2016 infovis16--2598591 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Optimizing Hierarchical Visualizations with the Minimum Description Length Principle. In this paper we examine how the Minimum Description Length (MDL) principle can be used to efficiently select aggregated views of hierarchical datasets that feature a good balance between clutter and information. We present MDL formulae for generating uneven tree cuts tailored to treemap and sunburst diagrams, taking into account the available display space and information content of the data. We present the results of a proof-of-concept implementation. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by implementing our approach in an existing visualization tool. Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory, which contains nearly half million categories. The results show that MDL views achieve near constant clutter level across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach. Collins, C. Veras, R. interaction treemap user study InfoVis clutter data models data visualization encoding histograms mathematical model visualization IEEE Transactions on Visualization and Computer Graphics antichain data aggregation hierarchy data multiscale visualization tree cut 2016 infovis16--2598619 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Multi-Granular Trend Detection for Time-Series Analysis. Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two- or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored. Dykes, J. Löffler, M. Speckmann, B. Staals, F. Van, G.A. time series InfoVis data models data visualization market research meteorology time series analysis uncertainty visualization IEEE Transactions on Visualization and Computer Graphics interactive exploration time series trend detection 2016 infovis16--2598862 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Map LineUps: Effects of spatial structure on graphical inference. Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s `Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n=361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference. Beecham, R. Dykes, J. Meulemans, W. Slingsby, A. Turkay, C. Wood, J. geographic geospatial theory InfoVis adaptation models correlation data visualization geography geospatial analysis testing visualization IEEE Transactions on Visualization and Computer Graphics geovisualization graphical inference just noticeable difference spatial autocorrelation statistical significance 2016 infovis16--2598885 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation. Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We conducted two quantitative user studies to evaluate different visual representations for such dense many-to-many flows. In our first study we compared a bundled node-link flow map representation and OD Maps [37] with a new visualisation we call MapTrix. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar. Dwyer, T. Goodwin, S. Marriott, K. Yang, Y. evaluation geographic matrix InfoVis algorithm design and analysis clutter data visualization joining processes labeling standards visualization IEEE Transactions on Visualization and Computer Graphics cartographic information visualisation flow maps matrix visualisation 2016 infovis16--2598609 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Iterating between Tools to Create and Edit Visualizations. A common workflow for visualization designers begins with a generative tool, like D3 or Processing, to create the initial visualization; and proceeds to a drawing tool, like Adobe Illustrator or Inkscape, for editing and cleaning. Unfortunately, this is typically a one-way process: once a visualization is exported from the generative tool into a drawing tool, it is difficult to make further, data-driven changes. In this paper, we propose a bridge model to allow designers to bring their work back from the drawing tool to re-edit in the generative tool. Our key insight is to recast this iteration challenge as a merge problem - similar to when two people are editing a document and changes between them need to reconciled. We also present a specific instantiation of this model, a tool called Hanpuku, which bridges between D3 scripts and Illustrator. We show several examples of visualizations that are iteratively created using Hanpuku in order to illustrate the flexibility of the approach. We further describe several hypothetical tools that bridge between other visualization tools to emphasize the generality of the model. Bigelow, A. Drucker, S.M. Fisher, D. Meyer, M. document insight InfoVis bridges data visualization image color analysis manuals software solid modeling visualization IEEE Transactions on Visualization and Computer Graphics illustration iteration visualization 2016 infovis16--2598498 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Investigating the Use of a Dynamic Physical Bar Chart for Data Exploration and Presentation. Physical data representations, or data physicalizations, are a promising new medium to represent and communicate data. Previous work mostly studied passive physicalizations which require humans to perform all interactions manually. Dynamic shape-changing displays address this limitation and facilitate data exploration tasks such as sorting, navigating in data sets which exceed the fixed size of a given physical display, or preparing “views” to communicate insights about data. However, it is currently unclear how people approach and interact with such data representations. We ran an exploratory study to investigate how non-experts made use of a dynamic physical bar chart for an open-ended data exploration and presentation task. We asked 16 participants to explore a data set on European values and to prepare a short presentation of their insights using a physical display. We analyze: (1) users' body movements to understand how they approach and react to the physicalization, (2) their hand-gestures to understand how they interact with physical data, (3) system interactions to understand which subsets of the data they explored and which features they used in the process, and (4) strategies used to explore the data and present observations. We discuss the implications of our findings for the use of dynamic data physicalizations and avenues for future work. Alexander, J. Hardy, J. Hornbæk, K. Jansen, Y. Taher, F. Woodruff, J. InfoVis bars cameras data visualization navigation pressing training IEEE Transactions on Visualization and Computer Graphics bar charts data presentation physical visualization physicalization shape-changing displays user behaviour 2016 infovis16--2599107 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display? High-quality immersive display technologies are becoming mainstream with the release of head-mounted displays (HMDs) such as the Oculus Rift. These devices potentially represent an affordable alternative to the more traditional, centralised CAVE-style immersive environments. One driver for the development of CAVE-style immersive environments has been collaborative sense-making. Despite this, there has been little research on the effectiveness of collaborative visualisation in CAVE-style facilities, especially with respect to abstract data visualisation tasks. Indeed, very few studies have focused on the use of these displays to explore and analyse abstract data such as networks and there have been no formal user studies investigating collaborative visualisation of abstract data in immersive environments. In this paper we present the results of the first such study. It explores the relative merits of HMD and CAVE-style immersive environments for collaborative analysis of network connectivity, a common and important task involving abstract data. We find significant differences between the two conditions in task completion time and the physical movements of the participants within the space: participants using the HMD were faster while the CAVE2 condition introduced an asymmetry in movement between collaborators. Otherwise, affordances for collaborative data analysis offered by the low-cost HMD condition were not found to be different for accuracy and communication with the CAVE2. These results are notable, given that the latest HMDs will soon be accessible (in terms of cost and potentially ubiquity) to a massive audience. Cordeil, M. Dwyer, T. Klein, K. Laha, B. Marriott, K. Thomas, B.H. network InfoVis collaboration data visualization navigation three-dimensional displays two dimensional displays virtual reality visualization IEEE Transactions on Visualization and Computer Graphics 3d network cave collaboration immersive analytics oculus rift 2016 infovis16--2599058 10/25/2016 IEEE Transactions on Visualization and Computer Graphics HindSight: Encouraging Exploration through Direct Encoding of Personal Interaction History. Physical and digital objects often leave markers of our use. Website links turn purple after we visit them, for example, showing us information we have yet to explore. These “footprints” of interaction offer substantial benefits in information saturated environments - they enable us to easily revisit old information, systematically explore new information, and quickly resume tasks after interruption. While applying these design principles have been successful in HCI contexts, direct encodings of personal interaction history have received scarce attention in data visualization. One reason is that there is little guidance for integrating history into visualizations where many visual channels are already occupied by data. More importantly, there is not firm evidence that making users aware of their interaction history results in benefits with regards to exploration or insights. Following these observations, we propose HindSight - an umbrella term for the design space of representing interaction history directly in existing data visualizations. In this paper, we examine the value of HindSight principles by augmenting existing visualizations with visual indicators of user interaction history (e.g. How the Recession Shaped the Economy in 255 Charts, NYTimes). In controlled experiments of over 400 participants, we found that HindSight designs generally encouraged people to visit more data and recall different insights after interaction. The results of our experiments suggest that simple additions to visualizations can make users aware of their interaction history, and that these additions significantly impact users' exploration and insights. Deng, C. Feng, M. Harrison, L. Peck, E.M. history interaction InfoVis context data analysis data visualization encoding history human computer interaction visualization IEEE Transactions on Visualization and Computer Graphics history interaction visualization 2016 infovis16--2598624 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data. We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions. Comba, J.L.D. Pahins, C.A.L. Scheidegger, C. Stephens, S.A. interaction InfoVis arrays big data data visualization memory management sorting visualization IEEE Transactions on Visualization and Computer Graphics data cube interactive exploration multidimensional data scalability 2016 infovis16--2598694 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets. Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: specifically, one can only quickly obtain plots such as histograms and heatmaps. In this paper, we contribute Gaussian Cubes, which significantly improves on state-of-the-art systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis (PCA). The fundamental insight in Gaussian Cubes is that instead of precomputing counts of many data subsets (as state-of-the-art systems do), Gaussian Cubes precomputes the best multivariate Gaussian for the respective data subsets. As an example, Gaussian Cubes can fit hundreds of models over millions of data points in well under a second, enabling novel types of visual exploration of such large datasets. We present three case studies that highlight the visualization and analysis capabilities in Gaussian Cubes, using earthquake safety simulations, astronomical catalogs, and transportation statistics. The dataset sizes range around one hundred million elements and 5 to 10 dimensions. We present extensive performance results, a discussion of the limitations in Gaussian Cubes, and future research directions. Bhaskar, A.S. Ferreira, N. Scheidegger, C. Wang, Z. Wei, Y. insight statistics InfoVis analytical models computational modeling data models data visualization manuals principal component analysis visualization IEEE Transactions on Visualization and Computer Graphics data cubes data modeling dimensionality reduction interactive visualization 2016 infovis16--2598586 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations. Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering. Carpendale, S. Collins, C. Loorak, M.H. Perin, C. InfoVis context data visualization encoding joining processes layout space exploration visualization IEEE Transactions on Visualization and Computer Graphics hybrid visualization multi-dimensional data 2016 infovis16--2598867 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Evaluation of Graph Sampling: A Visualization Perspective. Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have been proposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing is the impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies that investigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used in the graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results show that depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view to metric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies. Archambault, D. Cao, N. Cui, W. Qu, H. Shen, Q. Wu, Y. clustering evaluation graph metrics InfoVis clustering algorithms data mining data visualization fires measurement scalability visualization IEEE Transactions on Visualization and Computer Graphics empirical evaluation graph sampling graph visualization 2016 infovis16--2599106 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Evaluating the Impact of Binning 2D Scalar Fields. The expressiveness principle for visualization design asserts that a visualization should encode all of the available data, and only the available data, implying that continuous data types should be visualized with a continuous encoding channel. And yet, in many domains binning continuous data is not only pervasive, but it is accepted as standard practice. Prior work provides no clear guidance for when encoding continuous data continuously is preferable to employing binning techniques or how this choice affects data interpretation and decision making. In this paper, we present a study aimed at better understanding the conditions in which the expressiveness principle can or should be violated for visualizing continuous data. We provided participants with visualizations employing either continuous or binned greyscale encodings of geospatial elevation data and compared participants' ability to complete a wide variety of tasks. For various tasks, the results indicate significant differences in decision making, confidence in responses, and task completion time between continuous and binned encodings of the data. In general, participants with continuous encodings were faster to complete many of the tasks, but never outperformed those with binned encodings, while performance accuracy with binned encodings was superior to continuous encodings in some tasks. These findings suggest that strict adherence to the expressiveness principle is not always advisable. We discuss both the implications and limitations of our results and outline various avenues for potential work needed to further improve guidelines for using continuous versus binned encodings for continuous data types. Creem-Regehr, S.H. Meyer, M. Padilla, L. Quinan, P.S. geospatial InfoVis data visualization decision making encoding geospatial analysis guidelines image color analysis two dimensional displays IEEE Transactions on Visualization and Computer Graphics color perception geographic/geospatial visualization perceptual cognition qualitative evaluation 2016 infovis16--2598608 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Embedded Data Representations. We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles are making it increasingly easier to display data in-context. While researchers and artists have already begun to create embedded data representations, the benefits, trade-offs, and even the language necessary to describe and compare these approaches remain unexplored. In this paper, we formalize the notion of physical data referents - the real-world entities and spaces to which data corresponds - and examine the relationship between referents and the visual and physical representations of their data. We differentiate situated representations, which display data in proximity to data referents, and embedded representations, which display data so that it spatially coincides with data referents. Drawing on examples from visualization, ubiquitous computing, and art, we explore the role of spatial indirection, scale, and interaction for embedded representations. We also examine the tradeoffs between non-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications. Dragicevic, P. Jansen, Y. Willett, W. hardware interaction InfoVis augmented reality context data visualization instruments pipelines ubiquitous computing visualization IEEE Transactions on Visualization and Computer Graphics ambient displays augmented reality data physicalization information visualization ubiquitous computing 2016 infovis16--2598620 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Data-Driven Guides: Supporting Expressive Design for Information Graphics. In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics. Dontcheva, M. Kim, N.W. Li, W. Liu, Z. Pfister, H. Popovic, J. Schweickart, E. evaluation InfoVis data visualization design tools encoding manuals shape visualization IEEE Transactions on Visualization and Computer Graphics 2d graphics design tools information graphics visualization 2016 infovis16--2598918 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Colorgorical: Creating discriminable and preferable color palettes for information visualization. We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the user-defined importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which compare a potential sample to already-picked palette colors. After, a color is added to the palette by randomly sampling from the highest scoring palettes. Users can also specify hue ranges or build off their own starting palettes. This procedure differs from previous approaches that do not allow customization (e.g., pre-made ColorBrewer palettes) or do not consider visualization design constraints (e.g., Adobe Color and ACE). In a Palette Score Evaluation, we verified that each scoring function measured different color information. Experiment 1 demonstrated that slider manipulation generates palettes that are consistent with the expected balance of discriminability and aesthetic preference for 3-, 5-, and 8-color palettes, and also shows that the number of colors may change the effectiveness of pair-based discriminability and preference scores. For instance, if the Pair Preference slider were upweighted, users would judge the palettes as more preferable on average. Experiment 2 compared Colorgorical palettes to benchmark palettes (ColorBrewer, Microsoft, Tableau, Random). Colorgorical palettes are as discriminable and are at least as preferable or more preferable than the alternative palette sets. In sum, Colorgorical allows users to make customized color palettes that are, on average, as effective as current industry standards by balancing the importance of discriminability and aesthetic preference. Gramazio, C.C. Laidlaw, D.H. Schloss, K.B. categorical color evaluation experiment InfoVis benchmark testing color harmonic analysis image color analysis industries standards visualization IEEE Transactions on Visualization and Computer Graphics aesthetics in visualization color perception metrics & benchmarks visual design visualization 2016 infovis16--2598667 10/25/2016 IEEE Transactions on Visualization and Computer Graphics cite2vec: Citation-Driven Document Exploration via Word Embeddings. Effectively exploring and browsing document collections is a fundamental problem in visualization. Traditionally, document visualization is based on a data model that represents each document as the set of its comprised words, effectively characterizing what the document is. In this paper we take an alternative perspective: motivated by the manner in which users search documents in the research process, we aim to visualize documents via their usage, or how documents tend to be used. We present a new visualization scheme - cite2vec - that allows the user to dynamically explore and browse documents via how other documents use them, information that we capture through citation contexts in a document collection. Starting from a usage-oriented word-document 2D projection, the user can dynamically steer document projections by prescribing semantic concepts, both in the form of phrase/document compositions and document:phrase analogies, enabling the exploration and comparison of documents by their use. The user interactions are enabled by a joint representation of words and documents in a common high-dimensional embedding space where user-specified concepts correspond to linear operations of word and document vectors. Our case studies, centered around a large document corpus of computer vision research papers, highlight the potential for usage-based document visualization. Berger, M. McDonough, K. Seversky, L.M. document InfoVis context data visualization object detection semantics tracking two dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics document visualization word embeddings 2016 infovis16--2598518 10/25/2016 IEEE Transactions on Visualization and Computer Graphics booc.io: An Education System with Hierarchical Concept Maps and Dynamic Non-linear Learning Plans. Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present booc.io, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, booc.io is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into booc.io, and perform introductory qualitative evaluation with students. Fredericks, C. Higgins, D. Huff, C. King, G. Komisarchik, M. Pfister, H. Schwab, M. Strezhnev, A. Strobelt, H. Tompkin, J. education evaluation hierarchies hierarchy navigation InfoVis context data visualization education face navigation videos visualization IEEE Transactions on Visualization and Computer Graphics education hierarchies information visualization 2016 infovis16--2598647 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Authoring Data-Driven Videos with DataClips. Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven “clips” together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience. Amini, F. Irani, P.P. Lee, B. Monroy-Hernandez, A. Henry Riche, N. InfoVis animation data visualization libraries media videos visualization IEEE Transactions on Visualization and Computer Graphics authoring tools data storytelling data video narrative visualization visualization systems 2016 infovis16--2598898 10/25/2016 IEEE Transactions on Visualization and Computer Graphics An Evaluation of Visual Search Support in Maps. Visual search can be time-consuming, especially if the scene contains a large number of possibly relevant objects. An instance of this problem is present when using geographic or schematic maps with many different elements representing cities, streets, sights, and the like. Unless the map is well-known to the reader, the full map or at least large parts of it must be scanned to find the elements of interest. In this paper, we present a controlled eye-tracking study (30 participants) to compare four variants of map annotation with labels: within-image annotations, grid reference annotation, directional annotation, and miniature annotation. Within-image annotation places labels directly within the map without any further search support. Grid reference annotation corresponds to the traditional approach known from atlases. Directional annotation utilizes a label in combination with an arrow pointing in the direction of the label within the map. Miniature annotation shows a miniature grid to guide the reader to the area of the map in which the label is located. The study results show that within-image annotation is outperformed by all other annotation approaches. Best task completion times are achieved with miniature annotation. The analysis of eye-movement data reveals that participants applied significantly different visual task solution strategies for the different visual annotations. Balakrishnan, S. Burch, M. Hlawatsch, M. Netzel, R. Schmauder, H. Weiskopf, D. evaluation geographic InfoVis data visualization gaze tracking indexes search problems two dimensional displays urban areas visualization IEEE Transactions on Visualization and Computer Graphics eye tracking laboratory study map visualization visual search 2016 infovis16--2598920 10/25/2016 IEEE Transactions on Visualization and Computer Graphics VLAT: Development of a Visualization Literacy Assessment Test. The Information Visualization community has begun to pay attention to visualization literacy; however, researchers still lack instruments for measuring the visualization literacy of users. In order to address this gap, we systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement: (1) Test Blueprint Construction, (2) Test Item Generation, (3) Content Validity Evaluation, (4) Test Tryout and Item Analysis, (5) Test Item Selection, and (6) Reliability Evaluation. The VLAT consists of 12 data visualizations and 53 multiple-choice test items that cover eight data visualization tasks. The test items in the VLAT were evaluated with respect to their essentialness by five domain experts in Information Visualization and Visual Analytics (average content validity ratio = 0.66). The VLAT was also tried out on a sample of 191 test takers and showed high reliability (reliability coefficient omega = 0.76). In addition, we demonstrated the relationship between users' visualization literacy and aptitude for learning an unfamiliar visualization and showed that they had a fairly high positive relationship (correlation coefficient = 0.64). Finally, we discuss evidence for the validity of the VLAT and potential research areas that are related to the instrument. Kim, S.-H. Kwon, B.C. Lee, S. evaluation visual analytics InfoVis bars conferences data visualization instruments market research psychology reliability IEEE Transactions on Visualization and Computer Graphics aptitude assessment test education instrument measurement visualization literacy 2016 infovis16--2598496 10/25/2016 IEEE Transactions on Visualization and Computer Graphics PowerSet: A Comprehensive Visualization of Set Intersections. When analyzing a large amount of data, analysts often define groups over data elements that share certain properties. Using these groups as the unit of analysis not only reduces the data volume, but also allows detecting various patterns in the data. This involves analyzing intersection relations between these groups, and how the element attributes vary between these intersections. This kind of set-based analysis has various applications in a variety of domains, due to the generic and powerful notion of sets. However, visualizing intersections relations is challenging because their number grows exponentially with the number of sets. We present a novel technique based on Treemaps to provide a comprehensive overview of non-empty intersections in a set system in a scalable way. It enables gaining insight about how elements are distributed across these intersections as well as performing fine-grained analysis to explore and compare their attributes both in overview and in detail. Interaction allows querying and filtering these elements based on their set memberships. We demonstrate how our technique supports various use cases in data exploration and analysis by providing insights into set-based data, beyond the limits of state-of-the-art techniques. Alsallakh, B. Ren, L. insight interaction overview InfoVis bars data analysis data visualization layout scalability silicon visualization IEEE Transactions on Visualization and Computer Graphics interaction scalability set visualization treemaps 2016 infovis17--2743858 10/03/2017 IEEE Transactions on Visualization and Computer Graphics What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization. Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users. Crnovrsanin, T. Kwon, O. Ma, K.-L. graph machine learning metrics user study InfoVis data visualization inspection kernel layout measurement support vector machines visualization IEEE Transactions on Visualization and Computer Graphics aesthetics graph kernel graph layout graph visualization graphlet machine learning 2017 infovis17--2744118 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visualizing Nonlinear Narratives with Story Curves. In this paper, we present story curves, a visualization technique for exploring and communicating nonlinear narratives in movies. A nonlinear narrative is a storytelling device that portrays events of a story out of chronological order, e.g., in reverse order or going back and forth between past and future events. Many acclaimed movies employ unique narrative patterns which in turn have inspired other movies and contributed to the broader analysis of narrative patterns in movies. However, understanding and communicating nonlinear narratives is a difficult task due to complex temporal disruptions in the order of events as well as no explicit records specifying the actual temporal order of the underlying story. Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve. We also present Story Explorer, an interactive tool that visualizes a story curve together with complementary information such as characters and settings. Story Explorer further provides a script curation interface that allows users to specify the chronological order of events in movies. We used Story Explorer to analyze 10 popular nonlinear movies and describe the spectrum of narrative patterns that we discovered, including some novel patterns not previously described in the literature. Feedback from experts highlights potential use cases in screenplay writing and analysis, education and film production. A controlled user study shows that users with no expertise are able to understand visual patterns of nonlinear narratives using story curves. Bach, B. Gross, M.H. Im, H. Kim, N.W. Pfister, H. Schriber, S. education user study InfoVis games metadata motion pictures production tools visualization writing IEEE Transactions on Visualization and Computer Graphics nonlinear narrative storytelling visualization 2017 infovis17--2745141 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visual Exploration of Semantic Relationships in Neural Word Embeddings. Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools. Bremer, P.-T. Liu, S. Livnat, Y. Pascucci, V. Srikumar, V. Thiagarajan, J.J. Wang, B. uncertainty InfoVis data visualization natural language processing principal component analysis semantics tools visualization IEEE Transactions on Visualization and Computer Graphics high-dimensional data natural language processing word embedding 2017 infovis17--2744019 10/03/2017 IEEE Transactions on Visualization and Computer Graphics VisTiles: Coordinating and Combining Co-located Mobile Devices for Visual Data Exploration. We present VisTiles, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype. Dachselt, R. Horak, T. Langner, R. interaction multiple views user study InfoVis collaboration data visualization mobile communication prototypes smart phones visualization IEEE Transactions on Visualization and Computer Graphics coordinated & multiple views cross-device interaction mobile devices multi-display environment 2017 infovis17--2745941 10/03/2017 IEEE Transactions on Visualization and Computer Graphics The Hologram in My Hand: How Effective is Interactive Exploration of 3D Visualizations in Immersive Tangible Augmented Reality? We report on a controlled user study comparing three visualization environments for common 3D exploration. Our environments differ in how they exploit natural human perception and interaction capabilities. We compare an augmented-reality head-mounted display (Microsoft HoloLens), a handheld tablet, and a desktop setup. The novel head-mounted HoloLens display projects stereoscopic images of virtual content into a user's real world and allows for interaction in-situ at the spatial position of the 3D hologram. The tablet is able to interact with 3D content through touch, spatial positioning, and tangible markers, however, 3D content is still presented on a 2D surface. Our hypothesis is that visualization environments that match human perceptual and interaction capabilities better to the task at hand improve understanding of 3D visualizations. To better understand the space of display and interaction modalities in visualization environments, we first propose a classification based on three dimensions: perception, interaction, and the spatial and cognitive proximity of the two. Each technique in our study is located at a different position along these three dimensions. We asked 15 participants to perform four tasks, each task having different levels of difficulty for both spatial perception and degrees of freedom for interaction. Our results show that each of the tested environments is more effective for certain tasks, but that generally the desktop environment is still fastest and most precise in almost all cases. Bach, B. Beyer, J. Cordeil, M. Pfister, H. Sicat, R. interaction perception user study InfoVis augmented reality data visualization mice stereo image processing three-dimensional displays two dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics 3d interaction augmented reality immersive displays user study 2017 infovis17--2745878 10/03/2017 IEEE Transactions on Visualization and Computer Graphics The Explanatory Visualization Framework: An Active Learning Framework for Teaching Creative Computing Using Explanatory Visualizations. Visualizations are nowadays appearing in popular media and are used everyday in the workplace. This democratisation of visualization challenges educators to develop effective learning strategies, in order to train the next generation of creative visualization specialists. There is high demand for skilled individuals who can analyse a problem, consider alternative designs, develop new visualizations, and be creative and innovative. Our three-stage framework, leads the learner through a series of tasks, each designed to develop different skills necessary for coming up with creative, innovative, effective, and purposeful visualizations. For that, we get the learners to create an explanatory visualization of an algorithm of their choice. By making an algorithm choice, and by following an active-learning and project-based strategy, the learners take ownership of a particular visualization challenge. They become enthusiastic to develop good results and learn different creative skills on their learning journey. Headleand, C. Jackson, J.R. Ritsos, P.D. Roberts, J.C. InfoVis algorithm design and analysis computational modeling creativity data visualization education visualization IEEE Transactions on Visualization and Computer Graphics explanatory visualization information visualization learning support teaching visualization 2017 infovis17--2746018 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries. In this paper we present a set of four user studies aimed at exploring the visual design space of what we call keyword summaries: lists of words with associated quantitative values used to help people derive an intuition of what information a given document collection (or part of it) may contain. We seek to systematically study how different visual representations may affect people's performance in extracting information out of keyword summaries. To this purpose, we first create a design space of possible visual representations and compare the possible solutions in this design space through a variety of representative tasks and performance metrics. Other researchers have, in the past, studied some aspects of effectiveness with word clouds, however, the existing literature is somewhat scattered and do not seem to address the problem in a sufficiently systematic and holistic manner. The results of our studies showed a strong dependency on the tasks users are performing. In this paper we present details of our methodology, the results, as well as, guidelines on how to design effective keyword summaries based in our discoveries. Bertini, E. Felix, C. Franconeri, S. document metrics InfoVis data mining encoding extraterrestrial measurements layout systematics tag clouds visualization IEEE Transactions on Visualization and Computer Graphics keyword summaries tag clouds text visualization word clouds 2017 infovis17--2745298 10/03/2017 IEEE Transactions on Visualization and Computer Graphics TACO: Visualizing Changes in Tables Over Time. Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace “diff” tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selected tables. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios. Aigner, W. Grassinger, F. Hourieh, R. Niederer, C. Stitz, H. Streit, M. text InfoVis bars biology data visualization encoding games heating systems tools IEEE Transactions on Visualization and Computer Graphics difference visualization matrix table comparison 2017 infovis17--2743998 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Structuring Visualization Mock-Ups at the Graphical Level by Dividing the Display Space. Mock-ups are rapid, low fidelity prototypes, that are used in many design-related fields to generate and share ideas. While their creation is supported by many mature methods and tools, surprisingly few are suited for the needs of information visualization. In this article, we introduce a novel approach to creating visualizations mock-ups, based on a dialogue between graphic design and parametric toolkit explorations. Our approach consists in iteratively subdividing the display space, while progressively informing each division with realistic data. We show that a wealth of mock-ups can easily be created using only temporary data attributes, as we wait for more realistic data to become available. We describe the implementation of this approach in a D3-based toolkit, which we use to highlight its generative power, and we discuss the potential for transitioning towards higher fidelity prototypes. Boy, J. Vuillemot, R. toolkit InfoVis data visualization pipelines prototypes rendering (computer graphics) tools visualization IEEE Transactions on Visualization and Computer Graphics design methodologies graphic design mock-ups rapid prototyping toolkit design 2017 infovis17--2745140 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Stable Treemaps via Local Moves. Treemaps are a popular tool to visualize hierarchical data: items are represented by nested rectangles and the area of each rectangle corresponds to the data being visualized for this item. The visual quality of a treemap is commonly measured via the aspect ratio of the rectangles. If the data changes, then a second important quality criterion is the stability of the treemap: how much does the treemap change as the data changes. We present a novel stable treemapping algorithm that has very high visual quality. Whereas existing treemapping algorithms generally recompute the treemap every time the input changes, our algorithm changes the layout of the treemap using only local modifications. This approach not only gives us direct control over stability, but it also allows us to use a larger set of possible layouts, thus provably resulting in treemaps of higher visual quality compared to existing algorithms. We further prove that we can reach all possible treemap layouts using only our local modifications. Furthermore, we introduce a new measure for stability that better captures the relative positions of rectangles. We finally show via experiments on real-world data that our algorithm outperforms existing treemapping algorithms also in practice on either visual quality and/or stability. Our algorithm scores high on stability regardless of whether we use an existing stability measure or our new measure. Sondag, M. Speckmann, B. Verbeek, K. treemap InfoVis binary trees layout position measurement space exploration stability criteria visualization IEEE Transactions on Visualization and Computer Graphics local moves stability treemap 2017 infovis17--2744339 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Skeleton-Based Scagnostics. Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data. Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to automatically find potentially interesting plots, thereby making SPLOMs more scalable with the dimension count. While statistical measures such as regression lines can capture orientation, and graph-theoretic scagnostics measures can capture shape, there is no scatterplot characterization measure that uses both descriptors. Based on well-known results in shape analysis, we propose a scagnostics approach that captures both scatterplot shape and orientation using skeletons (or medial axes). Our representation can handle complex spatial distributions, helps discovery of principal trends in a multiscale way, scales visually well with the number of samples, is robust to noise, and is automatic and fast to compute. We define skeleton-based similarity metrics for the visual exploration and analysis of SPLOMs. We perform a user study to measure the human perception of scatterplot similarity and compare the outcome to our results as well as to graph-based scagnostics and other visual quality metrics. Our skeleton-based metrics outperform previously defined measures both in terms of closeness to perceptually-based similarity and computation time efficiency. Linsen, L. Matute, J. Telea, A.C. graph metrics perception scatterplot user study InfoVis correlation shape shape measurement skeleton two dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics high-dimensional data multidimensional data (primary keyword) 2017 infovis17--2744184 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Scatterplots: Tasks, Data, and Designs. Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots. Gleicher, M. Sarikaya, A. scatterplot InfoVis complexity theory correlation data visualization organizations taxonomy visualization IEEE Transactions on Visualization and Computer Graphics scatterplots study of designs task taxonomies 2017 infovis17--2745919 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization. We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes - an approach which previous methods cannot support. Chen, B. Deussen, O. Fu, C. Lu, K. Sedlmair, M. Sun, Y. Wang, Y. Zhu, L. cluster graph InfoVis computational modeling layout optimization shape springs stress visualization IEEE Transactions on Visualization and Computer Graphics constraints graph visualization stress majorization 2017 infovis17--2744138 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Priming and Anchoring Effects in Visualization. We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that - under certain circumstances - such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns. Sedlmair, M. Valdez, A.C. Ziefle, M. perception scatterplot InfoVis cognition correlation data models data visualization uncertainty visual perception visualization IEEE Transactions on Visualization and Computer Graphics anchoring bias mturk study perception scatterplots visualization 2017 infovis17--2745219 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks. Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces. Srinivasan, A. Stasko, J. evaluation interaction network InfoVis data visualization mice natural languages prototypes speech taxonomy visualization IEEE Transactions on Visualization and Computer Graphics direct manipulation multimodal interaction multitouch input natural language input network visualization 2017 infovis17--2745086 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Open vs. Closed Shapes: New Perceptual Categories? Effective communication using visualization relies in part on the use of viable encoding strategies. For example, a viewer's ability to rapidly and accurately discern between two or more categorical variables in a chart or figure is contingent upon the distinctiveness of the encodings applied to each variable. Research in perception suggests that color is a more salient visual feature when compared to shape and although that finding is supported by visualization studies, characteristics of shape also yield meaningful differences in distinctiveness. We propose that open or closed shapes (that is, whether shapes are composed of line segments that are bounded across a region of space or not) represent a salient characteristic that influences perceptual processing. Three experiments were performed to test the reliability of the open/closed category; the first two from the perspective of attentional allocation, and the third experiment in the context of multi-class scatterplot displays. In the first, a flanker paradigm was used to test whether perceptual load and open/closed feature category would modulate the effect of the flanker on target processing. Results showed an influence of both variables. The second experiment used a Same/Different reaction time task to replicate and extend those findings. Results from both show that responses are faster and more accurate when closed rather than open shapes are processed as targets, and there is more processing interference when two competing shapes come from the same rather than different open or closed feature categories. The third experiment employed three commonly used visual analytic tasks - perception of average value, numerosity, and linear relationships with both single and dual displays of open and closed symbols. Our findings show that for numerosity and trend judgments, in particular, that different symbols from the same open or closed feature category cause more perceptual interference when they are presented together in a plot than symbols from different categories. Moreover, the extent of the interference appears to depend upon whether the participant is focused on processing open or closed symbols. Burlinson, D. Goolkasian, P. Subramanian, K. categorical color experiment perception scatterplot InfoVis data visualization encoding interference shape visual analytics visual perception IEEE Transactions on Visualization and Computer Graphics closed shape open shape perceptual category scatterplot visualization design 2017 infovis17--2744018 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Nonlinear Dot Plots. Conventional dot plots use a constant dot size and are typically applied to show the frequency distribution of small data sets. Unfortunately, they are not designed for a high dynamic range of frequencies. We address this problem by introducing nonlinear dot plots. Adopting the idea of nonlinear scaling from logarithmic bar charts, our plots allow for dots of varying size so that columns with a large number of samples are reduced in height. For the construction of these diagrams, we introduce an efficient two-way sweep algorithm that leads to a dense and symmetrical layout. We compensate aliasing artifacts at high dot densities by a specifically designed low-pass filtering method. Examples of nonlinear dot plots are compared to conventional dot plots as well as linear and logarithmic histograms. Finally, we include feedback from an expert review. Rodrigues, N. Weiskopf, D. InfoVis algorithm design and analysis bars data visualization dynamic range histograms layout rendering (computer graphics) IEEE Transactions on Visualization and Computer Graphics layout nonlinear dot plot statistical graphics sweep algorithm 2017 infovis17--2743859 10/03/2017 IEEE Transactions on Visualization and Computer Graphics MyBrush: Brushing and Linking with Personal Agency. We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization. AndrĂ©, E. Carpendale, S. Koytek, P. Perin, C. Vermeulen, J. brushing interaction InfoVis brushes complexity theory data visualization image color analysis joining processes shape visualization IEEE Transactions on Visualization and Computer Graphics brushing coordinated multiple views design space information visualization interaction linking personal agency 2017 infovis17--2744359 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Modeling Color Difference for Visualization Design. Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples' abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design. Albers Szafir, D. color metrics InfoVis color computational modeling data visualization encoding image color analysis measurement visualization IEEE Transactions on Visualization and Computer Graphics color models color perception crowdsourcing graphical perception 2017 infovis17--2744158 10/03/2017 IEEE Transactions on Visualization and Computer Graphics LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks. Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks. Long-term usage data after putting the tool online revealed great interest in the machine learning community. Gehrmann, S. Pfister, H. Rush, A.M. Strobelt, H. machine learning InfoVis computational modeling data models pattern matching recurrent neural networks tools visualization IEEE Transactions on Visualization and Computer Graphics lstm machine learning recurrent neural networks visualization 2017 infovis17--2744198 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring. Visualizations often appear in multiples, either in a single display (e.g., small multiples, dashboard) or across time or space (e.g., slideshow, set of dashboards). However, existing visualization design guidelines typically focus on single rather than multiple views. Solely following these guidelines can lead to effective yet inconsistent views (e.g., the same field has different axes domains across charts), making interpretation slow and error-prone. Moreover, little is known how consistency balances with other design considerations, making it difficult to incorporate consistency mechanisms in visualization authoring software. We present a wizard-of-oz study in which we observed how Tableau users achieve and sacrifice consistency in an exploration-to-presentation visualization design scenario. We extend (from our prior work) a set of encoding-specific constraints defining consistency across multiple views. Using the constraints as a checklist in our study, we observed cases where participants spontaneously maintained consistent encodings and warned cases where consistency was overlooked. In response to the warnings, participants either revised views for consistency or stated why they thought consistency should be overwritten. We categorize participants' actions and responses as constraint validations and exceptions, depicting the relative importance of consistency and other design considerations under various circumstances (e.g., data cardinality, available encoding resources, chart layout). We discuss automatic consistency checking as a constraint-satisfaction problem and provide design implications for communicating inconsistencies to users. Hullman, J. Qu, Z. multiple views small multiples InfoVis adaptation models color data visualization encoding guidelines image color analysis visualization IEEE Transactions on Visualization and Computer Graphics evaluation qualitative study visualization design 2017 infovis17--2744218 10/03/2017 IEEE Transactions on Visualization and Computer Graphics iTTVis: Interactive Visualization of Table Tennis Data. The rapid development of information technology paved the way for the recording of fine-grained data, such as stroke techniques and stroke placements, during a table tennis match. This data recording creates opportunities to analyze and evaluate matches from new perspectives. Nevertheless, the increasingly complex data poses a significant challenge to make sense of and gain insights into. Analysts usually employ tedious and cumbersome methods which are limited to watching videos and reading statistical tables. However, existing sports visualization methods cannot be applied to visualizing table tennis competitions due to different competition rules and particular data attributes. In this work, we collaborate with data analysts to understand and characterize the sophisticated domain problem of analysis of table tennis data. We propose iTTVis, a novel interactive table tennis visualization system, which to our knowledge, is the first visual analysis system for analyzing and exploring table tennis data. iTTVis provides a holistic visualization of an entire match from three main perspectives, namely, time-oriented, statistical, and tactical analyses. The proposed system with several well-coordinated views not only supports correlation identification through statistics and pattern detection of tactics with a score timeline but also allows cross analysis to gain insights. Data analysts have obtained several new insights by using iTTVis. The effectiveness and usability of the proposed system are demonstrated with four case studies. Ji, C. Lan, J. Shu, X. Wang, J. Wu, Y. Zhang, H. Zhao, K. coordinated views statistics usability InfoVis correlation data visualization games hafnium mathematical model videos visualization IEEE Transactions on Visualization and Computer Graphics sports analytics sports visualization visual knowledge discovery visual knowledge representation 2017 infovis17--2743898 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty. People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively. Hullman, J. Kay, M. Kim, Y. Shrestha, S. experiment uncertainty InfoVis cognition data visualization probability distribution sociology statistics uncertainty visualization IEEE Transactions on Visualization and Computer Graphics graphical prediction interactive uncertainty visualization probability distribution replication crisis 2017 infovis17--2745978 10/03/2017 IEEE Transactions on Visualization and Computer Graphics HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples. This paper presents an interactive visualization interface-HiPiler-for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like “snippets”. Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data. Bach, B. Gehlenborg, N. Kerpedjiev, P. Lekschas, F. Pfister, H. brushing dimension reduction interaction matrix small multiples usability user study zoom InfoVis algorithm design and analysis bioinformatics data visualization genomics interviews visualization IEEE Transactions on Visualization and Computer Graphics biomedical visualization genomics interactive small multiples matrix comparison 2017 infovis17--2744338 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Functional Decomposition for Bundled Simplification of Trail Sets. Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization. Hurter, C. Nicol, F. Puechmorel, S. Telea, A.C. cluster graph InfoVis clutter data models data visualization shape splines (mathematics) three-dimensional displays two dimensional displays IEEE Transactions on Visualization and Computer Graphics edge bundles functional decomposition path generation path visualization streamlines trajectory visualization 2017 infovis17--2744320 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations. Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations. Heer, J. Mayhua, A. Poco, J. categorical color text InfoVis data mining data visualization encoding feature extraction image coding image color analysis optical character recognition software IEEE Transactions on Visualization and Computer Graphics chart understanding color computer vision information extraction redesign visualization 2017 infovis17--2745278 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections. Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the temporal analysis of a single attribute or the structural analysis of the multivariate data only. We present an approach where users can explore event sequences at multivariate and sequential level simultaneously by interactively defining a set of rewrite rules using multivariate regular expressions. Users can store resulting patterns as new types of events or attributes to interactively enrich or simplify event sequences for further investigation. In Eventpad we provide a bottom-up glyph-oriented approach for multivariate event sequence analysis by searching, clustering, and aligning them according to newly defined domain specific properties. We illustrate the effectiveness of our approach with real-world data sets including telecommunication traffic and hospital treatments. Cappers, B.C.M. van Wijk, J.J. clustering glyph history InfoVis communications technology data mining data visualization encoding hospitals sequences visualization IEEE Transactions on Visualization and Computer Graphics event visualization interaction multivariate events regular expressions sequence alignment 2017 infovis17--2745859 10/03/2017 IEEE Transactions on Visualization and Computer Graphics EdWordle: Consistency-Preserving Word Cloud Editing. We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself. Bao, C. Chen, B. Chu, X. Deussen, O. Sedlmair, M. Wang, Y. Zhu, L. InfoVis data visualization heuristic algorithms layout semantics tag clouds tools visualization IEEE Transactions on Visualization and Computer Graphics consistency text visualization wordle 2017 infovis17--2743939 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations. Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach. Divis, K.M. Haass, M.J. Matzen, L.E. Wang, Z. Wilson, A.T. color interaction InfoVis brain modeling data models data visualization measurement predictive models tools visualization IEEE Transactions on Visualization and Computer Graphics evaluation eye tracking visual saliency 2017 infovis17--2745240 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation. In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people's perspectives on the data. However, how such social information embedded in a visualization impacts a viewer's interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people's expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people's expectations on people's ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people's expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others' expectations alongside data. Hullman, J. Kim, Y. Reinecke, K. experiment social InfoVis collaboration data analysis data visualization focusing market research social network services uncertainty IEEE Transactions on Visualization and Computer Graphics data interpretation social influence social visualization 2017 infovis17--2744318 10/03/2017 IEEE Transactions on Visualization and Computer Graphics CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis. Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation. Höllt, T. Koning, F. Lelieveldt, B.P.F. Pezzotti, N. van Unen, V. Vilanova, A. evaluation field study hierarchies hierarchy navigation overview user study InfoVis biomedical imaging data visualization manuals navigation tools visualization IEEE Transactions on Visualization and Computer Graphics hierarchical data hsne single-cell analysis visual guidance 2017 infovis17--2744199 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Considerations for Visualizing Comparison. Supporting comparison is a common and diverse challenge in visualization. Such support is difficult to design because solutions must address both the specifics of their scenario as well as the general issues of comparison. This paper aids designers by providing a strategy for considering those general issues. It presents four considerations that abstract comparison. These considerations identify issues and categorize solutions in a domain independent manner. The first considers how the common elements of comparison-a target set of items that are related and an action the user wants to perform on that relationship-are present in an analysis problem. The second considers why these elements lead to challenges because of their scale, in number of items, complexity of items, or complexity of relationship. The third considers what strategies address the identified scaling challenges, grouping solutions into three broad categories. The fourth considers which visual designs map to these strategies to provide solutions for a comparison analysis problem. In sequence, these considerations provide a process for developers to consider support for comparison in the design of visualization tools. Case studies show how these considerations can help in the design and evaluation of visualization solutions for comparison problems. Gleicher, M. evaluation InfoVis complexity theory data visualization electronic mail encoding tools visualization IEEE Transactions on Visualization and Computer Graphics comparison information visualization task analysis taxonomies visualization models 2017 infovis17--2745138 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support. We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support. Bezerianos, A. Dimara, E. Dragicevic, P. metrics parallel coordinates scatterplot InfoVis data visualization decision making employment measurement tools visualization IEEE Transactions on Visualization and Computer Graphics decision making evaluation multidimensional visualization parallel coordinates scatterplot matrix tabular visualization 2017 infovis17--2745105 10/03/2017 IEEE Transactions on Visualization and Computer Graphics CasCADe: A Novel 4D Visualization System for Virtual Construction Planning. Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry. Barbosa, S.D. Celes, W. Ivson, P. Nascimento, D. InfoVis animation data visualization schedules solid modeling three-dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics design studies integrating spatial and non-spatial data visualization task and requirements analysis visualization in physical sciences and engineering 2017 infovis17--2743959 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Bubble Treemaps for Uncertainty Visualization. We present a novel type of circular treemap, where we intentionally allocate extra space for additional visual variables. With this extended visual design space, we encode hierarchically structured data along with their uncertainties in a combined diagram. We introduce a hierarchical and force-based circle-packing algorithm to compute Bubble Treemaps, where each node is visualized using nested contour arcs. Bubble Treemaps do not require any color or shading, which offers additional design choices. We explore uncertainty visualization as an application of our treemaps using standard error and Monte Carlo-based statistical models. To this end, we discuss how uncertainty propagates within hierarchies. Furthermore, we show the effectiveness of our visualization using three different examples: the package structure of Flare, the S&P 500 index, and the US consumer expenditure survey. Deussen, O. Görtler, J. Schulz, C. Weiskopf, D. color hierarchies treemap uncertainty InfoVis computational modeling data visualization indexes layout standards uncertainty visualization IEEE Transactions on Visualization and Computer Graphics circle packing contours hierarchy visualization tree layout treemaps uncertainty visualization 2017 infovis17--2744319 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Bridging from Goals to Tasks with Design Study Analysis Reports. Visualization researchers and practitioners engaged in generating or evaluating designs are faced with the difficult problem of transforming the questions asked and actions taken by target users from domain-specific language and context into more abstract forms. Existing abstract task classifications aim to provide support for this endeavour by providing a carefully delineated suite of actions. Our experience is that this bottom-up approach is part of the challenge: low-level actions are difficult to interpret without a higher-level context of analysis goals and the analysis process. To bridge this gap, we propose a framework based on analysis reports derived from open-coding 20 design study papers published at IEEE InfoVis 2009-2015, to build on the previous work of abstractions that collectively encompass a broad variety of domains. The framework is organized in two axes illustrated by nine analysis goals. It helps situate the analysis goals by placing each goal under axes of specificity (Explore, Describe, Explain, Confirm) and number of data populations (Single, Multiple). The single-population types are Discover Observation, Describe Observation, Identify Main Cause, and Collect Evidence. The multiple-population types are Compare Entities, Explain Differences, and Evaluate Hypothesis. Each analysis goal is scoped by an input and an output and is characterized by analysis steps reported in the design study papers. We provide examples of how we and others have used the framework in a top-down approach to abstracting domain problems: visualization designers or researchers first identify the analysis goals of each unit of analysis in an analysis stream, and then encode the individual steps using existing task classifications with the context of the goal, the level of specificity, and the number of populations involved in the analysis. Lam, H. Munzner, T. Tory, M. design study InfoVis bridges data analysis data visualization market research sociology statistics visualization IEEE Transactions on Visualization and Computer Graphics analysis goals data analysis design studies framework open coding task classifications 2017 infovis17--2744298 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Blinded with Science or Informed by Charts? A Replication Study. We provide a reappraisal of Tal and Wansink's study “Blinded with Science”, where seemingly trivial charts were shown to increase belief in drug efficacy, presumably because charts are associated with science. Through a series of four replications conducted on two crowdsourcing platforms, we investigate an alternative explanation, namely, that the charts allowed participants to better assess the drug's efficacy. Considered together, our experiments suggest that the chart seems to have indeed promoted understanding, although the effect is likely very small. Meanwhile, we were unable to replicate the original study's findings, as text with chart appeared to be no more persuasive - and sometimes less persuasive - than text alone. This suggests that the effect may not be as robust as claimed and may need specific conditions to be reproduced. Regardless, within our experimental settings and considering our study as a whole (<inline-formula><tex-math notation=\ Dragicevic, P. Jansen, Y. text InfoVis bars data mining data visualization drugs sociology statistics IEEE Transactions on Visualization and Computer Graphics charts data comprehension methodology persuasion replication study 2017 infovis17--2743918 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Assessing the Graphical Perception of Time and Speed on 2D+Time Trajectories. We empirically evaluate the extent to which people perceive non-constant time and speed encoded on 2D paths. In our graphical perception study, we evaluate nine encodings from the literature for both straight and curved paths. Visualizing time and speed information is a challenge when the x and y axes already encode other data dimensions, for example when plotting a trip on a map. This is particularly true in disciplines such as time-geography and movement analytics that often require visualizing spatio-temporal trajectories. A common approach is to use 2D+time trajectories, which are 2D paths for which time is an additional dimension. However, there are currently no guidelines regarding how to represent time and speed on such paths. Our study results provide InfoVis designers with clear guidance regarding which encodings to use and which ones to avoid; in particular, we suggest using color value to encode speed and segment length to encode time whenever possible. Carpendale, S. Perin, C. Pusch, R. Wun, T. color perception InfoVis data visualization encoding guidelines image color analysis trajectory two dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics graphical perception movement data quantitative evaluation trajectory visualization visual encoding 2017 infovis17--2745958 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Active Reading of Visualizations. We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience. Carpendale, S. Huron, S. Perin, C. Pusch, R. Walny, J. Wun, T. text InfoVis collaboration data visualization decoding education indexes systematics visualization IEEE Transactions on Visualization and Computer Graphics active reading active reading of visualizations information visualization spectrum of physical engagement 2017 vast06--4035741 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Time Tree: Exploring Time Changing Hierarchies. Intelligence analysis often involves the task of gathering information about an organization. Knowledge about individuals in an organization and their relationships, often represented as a hierarchical organization chart, is crucial for understanding the organization. However, it is difficult for intelligence analysts to follow all individuals in an organization. Existing hierarchy visualizations have largely focused on the visualization of fixed structures and can not effectively depict the evolution of a hierarchy over time. We introduce TimeTree, a novel visualization tool designed to enable exploration of a changing hierarchy. TimeTree enables analysts to navigate the history of an organization, identify events associated with a specific entity (visualized on a TimeSlider), and explore an aggregate view of an individual's career path (a CareerTree). We demonstrate the utility of TimeTree by investigating a set of scenarios developed by an expert intelligence analyst. The scenarios are evaluated using a real dataset composed of eighteen thousand career events from more than eight thousand individuals. Insights gained from this analysis are presented. Bodnar, J.W. Card, S.K. Heer, J. Pendleton, B.A. Suh, B. hierarchies hierarchy history intelligence analysis VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) DOI tree organizational chart time series data timetree tree visualization visual analytics 2006 vast06--4035742 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Exploration of Spatio-temporal Relationships for Scientific Data. Spatio-temporal relationships among features extracted from temporally-varying scientific datasets can provide useful information about the evolution of an individual feature and its interactions with other features. However, extracting such useful relationships without user guidance is cumbersome and often an error prone process. In this paper, we present a visual analysis system that interactively discovers such relationships from the trajectories of derived features. We describe analysis algorithms to derive various spatial and spatio-temporal relationships. A visual interface is presented using which the user can interactively select spatial and temporal extents to guide the knowledge discovery process. We show the usefulness of our proposed algorithms on datasets originating from computational fluid dynamics. We also demonstrate how the derived relationships can help in explaining the occurrence of critical events like merging and bifurcation of the vortices. Machiraju, R. Mehta, S. Parthasarathy, S. VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035743 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analytics of Paleoceanographic Conditions. Decade scale oceanic phenomena like El Nino are correlated with weather anomalies all over the globe. Only by understanding the events that produced the climatic conditions in the past will it be possible to forecast abrupt climate changes and prevent disastrous consequences for human beings and their environment. Paleoceanography research is a collaborative effort that requires the analysis of paleo time-series, which are obtained from a number of independent techniques and instruments and produced by a variety of different researchers and/or laboratories. The complexity of these phenomena that consist of massive, dynamic and often conflicting data can only be faced by means of analytical reasoning supported by a highly interactive visual interface. This paper presents an interactive visual analysis environment for paleoceanography that permits to gain insight into the paleodata and allow the control and steering of the analytical methods involved in the reconstruction of the climatic conditions of the past. Theron, R. insight visual analytics VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035744 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Avian Flu Case Study with nSpace and GeoTime. GeoTime and nSpace are new analysis tools that provide innovative visual analytic capabilities. This paper uses an epidemiology analysis scenario to illustrate and discuss these new investigative methods and techniques. In addition, this case study is an exploration and demonstration of the analytical synergy achieved by combining GeoTime's geo-temporal analysis capabilities, with the rapid information triage, scanning and sense-making provided by nSpace. A fictional analyst works through the scenario from the initial brainstorming through to a final collaboration and report. With the efficient knowledge acquisition and insights into large amounts of documents, there is more time for the analyst to reason about the problem and imagine ways to mitigate threats. The use of both nSpace and GeoTime initiated a synergistic exchange of ideas, where hypotheses generated in either software tool could be cross-referenced, refuted, and supported by the other tool. Bodnar, A. Harper, R. Proulx, P. Schroh, D. Tandon, S. Wright, W. case study collaboration VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) geo-spatial information systems human information interaction information visualization sensemaking temporal analysis user centered design visual analytics 2006 vast06--4035745 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analysis of Historic Hotel Visitation Patterns. Understanding the space and time characteristics of human interaction in complex social networks is a critical component of visual tools for intelligence analysis, consumer behavior analysis, and human geography. Visual identification and comparison of patterns of recurring events is an essential feature of such tools. In this paper, we describe a tool for exploring hotel visitation patterns in and around Rebersburg, Pennsylvania from 1898-1900. The tool uses a wrapping spreadsheet technique, called reruns, to display cyclic patterns of geographic events in multiple overlapping natural and artificial calendars. Implemented as an improvise visualization, the tool is in active development through a iterative process of data collection, hypothesis, design, discovery, and evaluation in close collaboration with historical geographers. Several discoveries have inspired ongoing data collection and plans to expand exploration to include historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in numerous feature and design recommendations. Fyfe, D. Holdsworth, D. MacEachren, A.M. Peuquet, D.J. Robinson, A.C. Weaver, C. collaboration evaluation geographic intelligence analysis interaction social usability VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035746 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. Visualizing and analyzing social networks is a challenging problem that has been receiving growing attention. An important first step, before analysis can begin, is ensuring that the data is accurate. A common data quality problem is that the data may inadvertently contain several distinct references to the same underlying entity; the process of reconciling these references is called entity-resolution. D-Dupe is an interactive tool that combines data mining algorithms for entity resolution with a task-specific network visualization. Users cope with complexity of cleaning large networks by focusing on a small subnetwork containing a potential duplicate pair. The subnetwork highlights relationships in the social network, making the common relationships easy to visually identify. D-Dupe users resolve ambiguities either by merging nodes or by marking them distinct. The entity resolution process is iterative: as pairs of nodes are resolved, additional duplicates may be revealed; therefore, resolution decisions are often chained together. We give examples of how users can flexibly apply sequences of actions to produce a high quality entity resolution result. We illustrate and evaluate the benefits of D-Dupe on three bibliographic collections. Two of the datasets had already been cleaned, and therefore should not have contained duplicates; despite this fact, many duplicates were rapidly identified using D-Dupe's unique combination of entity resolution algorithms within a task-specific visual interface. Bilgic, M. Getoor, L. Licamele, L. Shneiderman, B. data mining network social VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035747 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Interactive Visual Synthesis of Analytic Knowledge. A visual investigation involves both the examination of existing information and the synthesis of new analytic knowledge. This is a progressive process in which newly synthesized knowledge becomes the foundation for future discovery. In this paper, we present a novel system supporting interactive, progressive synthesis of analytic knowledge. Here we use the term "analytic knowledge" to refer to concepts that a user derives from existing data along with the evidence supporting such concepts. Unlike existing visual analytic-tools, which typically support only exploration of existing information, our system offers two unique features. First, we support user-system cooperative visual synthesis of analytic knowledge from existing data. Specifically, users can visually define new concepts by annotating existing information, and refine partially formed concepts by linking additional evidence or manipulating related concepts. In response to user actions, our system can automatically manage the evolving corpus of synthesized knowledge and its corresponding evidence. Second, we support progressive visual analysis of synthesized knowledge. This feature allows analysts to visually explore both existing knowledge and synthesized knowledge, dynamically incorporating earlier analytic conclusions into the ensuing discovery process. We have applied our system to two complex but very different analytic applications. Our preliminary evaluation shows the promise of our work. Aggarwal, V. Gotz, D. Zhou, M.X. evaluation VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035748 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analysis of Conflicting Opinions. Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da Vinci Code, including 1,738 positive and 918 negative reviews. The study is motivated by critical questions such as: what are the differences between positive and negative reviews? What is the origin of a particular opinion? How do these opinions change over time? To what extent can differentiating features be identified from unstructured text? How accurately can these features predict the category of a review? We first analyze terminology variations in these reviews in terms of syntactic, semantic, and statistic associations identified by TermWatch and use term variation patterns to depict underlying topics. We then select the most predictive terms based on log likelihood tests and demonstrate that this small set of terms classifies over 70% of the conflicting reviews correctly. This feature selection process reduces the dimensionality of the feature space from more than 20,000 dimensions to a couple of hundreds. We utilize automatically generated decision trees to facilitate the understanding of conflicting opinions in terms of these highly predictive terms. This study also uses a number of visualization and modeling tools to identify not only what positive and negative reviews have in common, but also they differ and evolve over time. Chen, C. Ibekwe-SanJuan, F. SanJuan, E. Weaver, C. text VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) conflicting opinions decision tree predictive text analysis sensemaking terminology variation visual analytics 2006 vast06--4035749 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Have Green A˘Ż A Visual Analytics Framework for Large Semantic Graphs. A semantic graph is a network of heterogeneous nodes and links annotated with a domain ontology. In intelligence analysis, investigators use semantic graphs to organize concepts and relationships as graph nodes and links in hopes of discovering key trends, patterns, and insights. However, as new information continues to arrive from a multitude of sources, the size and complexity of the semantic graphs will soon overwhelm an investigator's cognitive capacity to carry out significant analyses. We introduce a powerful visual analytics framework designed to enhance investigators' natural analytical capabilities to comprehend and analyze large semantic graphs. The paper describes the overall framework design, presents major development accomplishments to date, and discusses future directions of a new visual analytics system known as Have Green. Chin, G. Foote, H. Mackey, P. Thomas, J. Wong, P.C. graph intelligence analysis network visual analytics VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035750 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Exploring Large-Scale Video News via Interactive Visualization. In this paper, we have developed a novel visualization framework to enable more effective visual analysis of large-scale news videos, where keyframes and keywords are automatically extracted from news video clips and visually represented according to their interestingness measurement to help audiences rind news stories of interest at first glance. A computational approach is also developed to quantify the interestingness measurement of video clips. Our experimental results have shown that our techniques for intelligent news video analysis have the capacity to enable more effective visualization of large-scale news videos. Our news video visualization system is very useful for security applications and for general audiences to quickly find news topics of interest from among many channels. Fan, J. Luo, H. Ribarsky, W. Satoh, S. Yang, J. security VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035751 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Interactive Visualization and Analysis of Network and Sensor Data on Mobile Devices. Mobile devices are rapidly gaining popularity due to their small size and their wide range of functionality. With the constant improvement in wireless network access, they are an attractive option not only for day to day use. but also for in-field analytics by first responders in widespread areas. However, their limited processing, display, graphics and power resources pose a major challenge in developing effective applications. Nevertheless, they are vital for rapid decision making in emergencies when combined with appropriate analysis tools. In this paper, we present an efficient, interactive visual analytic system using a PDA to visualize network information from Purdue's Ross-Ade Stadium during football games as an example of in-held data analytics combined with text and video analysis. With our system, we can monitor the distribution of attendees with mobile devices throughout the stadium through their access of information and association/disassociation from wireless access points, enabling the detection of crowd movement and event activity. Through correlative visualization and analysis of synchronized video (instant replay video) and text information (play statistics) with the network activity, we can provide insightful information to network monitoring personnel, safety personnel and analysts. This work provides a demonstration and testbed for mobile sensor analytics that will help to improve network performance and provide safety personnel with information for better emergency planning and guidance. Aulf, A. Bue, B. Coyle, E. Ebert, D.S. Jang, Y. Pattath, A. Zhong, X. network statistics text VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035752 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) NetLens: Iterative Exploration of Content-Actor Network Data. Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract content-actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract content-actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract data model. This paper describes NetLens applying a subset of the ACM Digital Library consisting of about 4,000 papers from the CM I conference written by about 6,000 authors. In addition, we are now working on a collection of half a million emails, and a dataset of legal cases. Bederson, B.B. Kang, H. Lee, B. Plaisant, C. network VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) content-actor network data digital library human-computer interaction incremental data exploration information visualization iterative query refinement network visualization piccolo user interface 2006 vast06--4035753 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Interactive Wormhole Detection in Large Scale Wireless Networks. Wormhole attacks in wireless networks can severely deteriorate the network performance and compromise the security through spoiling the routing protocols and weakening the security enhancements. This paper develops an approach, interactive visualization of wormholes (IVoW), to monitor and detect such attacks in large scale wireless networks in real time. We characterize the topology features of a network under wormhole attacks through the node position changes and visualize the information at dynamically adjusted scales. We integrate an automatic detection algorithm with appropriate user interactions to handle complicated scenarios that include a large number of moving nodes and multiple worm-hole attackers. Various visual forms have been adopted to assist the understanding and analysis of the reconstructed network topology and improve the detection accuracy. Extended simulation has demonstrated that the proposed approach can effectively locate the fake neighbor connections without introducing many false alarms. IVoW does not require the wireless nodes to be equipped with any special hardware, thus avoiding any additional cost. The proposed approach demonstrates that interactive visualization can be successfully combined with network security mechanisms to greatly improve the intrusion detection capabilities. Lu, A. Wang, W. hardware network security VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035754 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Enhancing Visual Analysis of Network Traffic Using a Knowledge Representation. This paper presents a network traffic analysis system that couples visual analysis with a declarative knowledge representation. The system supports multiple iterations of the sense-making loop of analytic reasoning by allowing users to save discoveries as they are found and to reuse them in future iterations. We show how the knowledge representation can be used to improve both the visual representations and the basic analytical tasks of filtering and changing level of detail. We describe how the system can be used to produce models of network patterns, and show results from classifying one day of network traffic in our laboratory. Gerth, J. Hanrahan, P. Xiao, L. network VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) network traffic visualization visual analysis 2006 vast06--4035755 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Accelerating Network Traffic Analytics Using Query-Driven Visualization. Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the time required for data filtering, querying, hypothesis testing and knowledge discovery in the domain of network connection data analysis. We show that use of compressed bitmap indexing can quickly answer queries in an interactive visual data analysis application, and compare its performance with two alternatives for serial and parallel filtering/querying on 2.5 billion records' worth of network connection data collected over a period of 42 weeks. Our approach to visual network connection data exploration centers on two primary factors: interactive ad-hoc and multiresolution query formulation and execution over n dimensions and visual display of the n-dimensional histogram results. This combination is applied in a case study to detect a distributed network scan and to then identify the set of remote hosts participating in the attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools. Bethel, E.W. Campbell, S. Dart, E. Stockinger, K. Wu, K. case study network VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) data mining network security query-driven visualization visual analytics 2006 vast06--4035756 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Monitoring Network Traffic with Radial Traffic Analyzer. Extensive spread of malicious code on the Internet and also within intranets has risen the user's concern about what kind of data is transferred between her or his computer and other hosts on the network. Visual analysis of this kind of information is a challenging task, due to the complexity and volume of the data type considered, and requires special design of appropriate visualization techniques. In this paper, we present a scalable visualization toolkit for analyzing network activity of computer hosts on a network. The visualization combines network packet volume and type distribution information with geographic information, enabling the analyst to use geographic distortion techniques such as the HistoMap technique to become aware of the traffic components in the course of the analysis. The presented analysis tool is especially useful to compare important network load characteristics in a geographically aware display, to relate communication partners, and to identify the type of network traffic occurring. The results of the analysis are helpful in understanding typical network communication activities, and in anticipating potential performance bottlenecks or problems. It is suited for both off-line analysis of historic data, and via animation for on-line monitoring of packet-based network traffic in real time. Keim, D.A. Mansmann, F. Schneidewind, J. Schreck, T. animation distortion geographic network radial toolkit VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) geography-based solutions information visualization network traffic monitoring visual analytics 2006 vast06--4035757 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Toward a Multi-Analyst, Collaborative Framework for Visual Analytics. We describe a framework for the display of complex, multidimensional data, designed to facilitate exploration, analysis, and collaboration among multiple analysts. This framework aims to support human collaboration by making it easier to share representations, to translate from one point of view to another, to explain arguments, to update conclusions when underlying assumptions change, and to justify or account for decisions or actions. Multidimensional visualization techniques are used with interactive, context-sensitive, and tunable graphs. Visual representations are flexibly generated using a knowledge representation scheme based on annotated logic; this enables not only tracking and fusing different viewpoints, but also unpacking them. Fusing representations supports the creation of multidimensional meta-displays as well as the translation or mapping from one point of view to another. At the same time, analysts also need to be able to unpack one another's complex chains of reasoning, especially if they have reached different conclusions, and to determine the implications, if any, when underlying assumptions or evidence turn out to be false. The framework enables us to support a variety of scenarios as well as to systematically generate and test experimental hypotheses about the impact of different kinds of visual representations upon interactive collaboration by teams of distributed analysts. Brennan, S.E. Kaufman, A. Mueller, K. Ramakrishnan, I.V. Warren, D.S. Zelinsky, G. collaboration visual analytics VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) collaborative and distributed visualization data management and knowledge representation visual analytics visual knowledge discovery 2006 vast06--4035758 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Collaborative Visual Analytics: Inferring from the Spatial Organization and Collaborative Use of Information. We introduce a visual analytics environment for the support of remote-collaborative sense-making activities. Team members use their individual graphical interfaces to collect, organize and comprehend task-relevant information relative to their areas of expertise. A system of computational agents infers possible relationships among information items through the analysis of the spatial and temporal organization and collaborative use of information. The computational agents support the exchange of information among team members to converge their individual contributions. Our system allows users to navigate vast amounts of shared information effectively and remotely dispersed team members to work independently without diverting from common objectives as well as to minimize the necessary amount of verbal communication. Keel, P.E. visual analytics VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035759 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Beyond Usability: Evaluation Aspects of Visual Analytic Environments. A new field of research, visual analytics, has been introduced. This has been defined as "the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas and Cook, 2005). Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation, and dissemination. As researchers begin to develop visual analytic environments, it is advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work has on the users who work in such environments. This paper presents five areas or aspects of visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as the five evaluation areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined. Scholtz, J. awareness collaboration evaluation interaction metrics usability visual analytics VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) analytic environments metrics visualization 2006 vast06--4035760 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visualizing the Performance of Computational Linguistics Algorithms. We have built a visualization system and analysis portal for evaluating the performance of computational linguistics algorithms. Our system focuses on algorithms that classify and cluster documents by assigning weights to words and scoring each document against high dimensional reference concept vectors. The visualization and algorithm analysis techniques include confusion matrices, ROC curves, document visualizations showing word importance, and interactive reports. One of the unique aspects of our system is that the visualizations are thin-client Web-based components built using SVG visualization components. Eick, S.G. Mauger, J. Ratner, A. cluster document VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) AJAX ROC curves SVG confusion matrices document categorization thin-client 2006 vast06--4035761 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Scentindex: Conceptually Reorganizing Subject Indexes for Reading. A great deal of analytical work is done in the context of reading, in digesting the semantics of the material, the identification of important entities, and capturing the relationship between entities. Visual analytic environments, therefore, must encompass reading tools that enable the rapid digestion of large amount of reading material. Other than plain text search, subject indexes, and basic highlighting, tools are needed for rapid foraging of text. In this paper, we describe a technique that presents an enhanced subject index for a book by conceptually reorganizing it to suit particular expressed user information needs. Users first enter information needs via keywords describing the concepts they are trying to retrieve and comprehend. Then our system, called ScentIndex, computes what index entries are conceptually related and reorganizes and displays these index entries on a single page. We also provide a number of navigational cues to help users peruse over this list of index entries and find relevant passages quickly. Compared to regular reading of a paper book, our study showed that users are more efficient and more accurate in finding, comparing, and comprehending material in our system. Card, S.K. Chi, E.H. Heiser, J. Hong, L. text VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) book index contextualization ebooks information scent personalized information access 2006 vast06--4035762 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories. Finding patterns of events over time is important in searching patient histories, Web logs, news stories, and criminal activities. This paper presents PatternFinder, an integrated interface for query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets. We define temporal patterns as sequences of events with inter-event time spans. PatternFinder allows users to specify the attributes of events and time spans to produce powerful pattern queries that are difficult to express with other formalisms. We characterize the range of queries PatternFinder supports as users vary the specificity at which events and time spans are defined. Pattern Finder's query capabilities together with coupled ball-and-chain and tabular visualizations enable users to effectively query, explore and analyze event patterns both within and across data entities (e.g. patient histories, terrorist groups, Web logs, etc.). Fails, J.A. Karlson, A.K. Shahamat, L. Shneiderman, B. categorical VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) information visualization temporal query user interface 2006 vast06--4035763 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) User Interfaces for the Exploration of Hierarchical Multi-dimensional Data. A variety of user interfaces have been developed to support the querying of hierarchical multi-dimensional data in an OLAP setting such as pivot tables and Polaris. They are used to regularly check portions of a dataset and to explore a new dataset for the first time. In this paper, we establish criteria for OLAP user interface capabilities to facilitate comparison. Two criteria are the number of displayed dimensions along which comparisons can be made and the number of dimensions that are viewable at once - visual comparison depth and width. We argue that interfaces with greater visual comparison depth support regular checking of known data by users that know roughly where to look, while interfaces with greater comparison width support exploration of new data by users that have no a priori starting point and need to scan all dimensions. Pivot tables and Polaris are examples of the former. The main contribution of this paper is to introduce a new scalable interface that uses parallel dimension axis which supports the latter, greater visual comparison width. We compare our approach to both table based and parallel coordinate based interfaces. We present an implementation of our interface SGViewer, user scenarios and provide an evaluation that supports the usability of our interface. Sifer, M. evaluation usability VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) OLAP data exploration parallel coordinates visualization 2006 vast06--4035764 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Exploratory Visualization of Multivariate Data with Variable Quality. Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps in data gathering. Analysis conducted on variable quality data can lead to inaccurate or incorrect results. An effective visualization system must make users aware of the quality of their data by explicitly conveying not only the actual data content, but also its quality attributes. While some research has been conducted on visualizing uncertainty in spatio-temporal data and univariate data, little work has been reported on extending this capability into multivariate data visualization. In this paper we describe our approach to the problem of visually exploring multivariate data with variable quality. As a foundation, we propose a general approach to defining quality measures for tabular data, in which data may experience quality problems at three granularities: individual data values, complete records, and specific dimensions. We then present two approaches to visual mapping of quality information into display space. In particular, one solution embeds the quality measures as explicit values into the original dataset by regarding value quality and record quality as new data dimensions. The other solution is to superimpose the quality information within the data visualizations using additional visual variables. We also report on user studies conducted to assess alternate mappings of quality attributes to visual variables for the second method. In addition, we describe case studies that expose some of the advantages and disadvantages of these two approaches. Huang, S. Rundensteiner, E.A. Ward, M.O. Xie, Z. uncertainty VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035765 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Semantic Image Browser: Bridging Information Visualization with Automated Intelligent Image Analysis. Browsing and retrieving images from large image collections are becoming common and important activities. Semantic image analysis techniques, which automatically detect high level semantic contents of images for annotation, are promising solutions toward this problem. However, few efforts have been made to convey the annotation results to users in an intuitive manner to enable effective image browsing and retrieval. There is also a lack of methods to monitor and evaluate the automatic image analysis algorithms due to the high dimensional nature of image data, features, and contents. In this paper, we propose a novel, scalable semantic image browser by applying existing information visualization techniques to semantic image analysis. This browser not only allows users to effectively browse and search in large image databases according to the semantic content of images, but also allows analysts to evaluate their annotation process through interactive visual exploration. The major visualization components of this browser are multi-dimensional scaling (MDS) based image layout, the value and relation (VaR) display that allows effective high dimensional visualization without dimension reduction, and a rich set of interaction tools such as search by sample images and content relationship detection. Our preliminary user study showed that the browser was easy to use and understand, and effective in supporting image browsing and retrieval tasks. Fan, J. Gao, Y. Hubball, D. Luo, H. Ribarsky, W. Ward, M.O. Yang, J. dimension reduction interaction user study VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast06--4035766 10/31/2006 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) Pixnostics: Towards Measuring the Value of Visualization. During the last two decades a wide variety of advanced methods for the visual exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which a user or an analyst has to select the right parameter settings from among many or select a subset of the available attribute space for the visualization process, in order to construct valuable visualizations that provide insight, into the data and reveal interesting patterns. The right choice of input parameters is often essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. In this paper we propose a novel method for automatically determining meaningful parameter- and attribute settings based on the information content of the resulting visualizations. Our technique called Pixnostics, in analogy to Scagnostics (Wilkinson et al., 2005), automatically analyses pixel images resulting from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real world applications are provided to show the benefit of the proposed approach. Keim, D.A. Schneidewind, J. Sips, M. insight interaction pixel VAST 2006 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2006 vast07--4388990 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Activity Analysis Using Spatio-Temporal Trajectory Volumes in Surveillance Applications. In this paper, we present a system to analyze activities and detect anomalies in a surveillance application, which exploits the intuition and experience of security and surveillance experts through an easy- to-use visual feedback loop. The multi-scale and location specific nature of behavior patterns in space and time is captured using a wavelet-based feature descriptor. The system learns the fundamental descriptions of the behavior patterns in a semi-supervised fashion by the higher order singular value decomposition of the space described by the training data. This training process is guided and refined by the users in an intuitive fashion. Anomalies are detected by projecting the test data into this multi-linear space and are visualized by the system to direct the attention of the user to potential problem spots. We tested our system on real-world surveillance data, and it satisfied the security concerns of the environment. Irfanoglu, O. Janoos, F. Machiraju, R. Parent, R. Singh, S. security VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) HOSVD anomaly detection surveillance trajectory wavelets 2007 vast07--4388991 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) FemaRepViz: Automatic Extraction and Geo-Temporal Visualization of FEMA National Situation Updates. An architecture for visualizing information extracted from text documents is proposed. In conformance with this architecture, a toolkit, FemaRepViz, has been implemented to extract and visualize temporal, geospatial, and summarized information from FEMA national update reports. Preliminary tests have shown satisfactory accuracy for FEMARepViz. A central component of the architecture is an entity extractor that extracts named entities like person names, location names, temporal references, etc. FEMARepViz is based on FactXtractor, an entity-extractor that works on text documents. The information extracted using FactXtractor is processed using GeoTagger, a geographical name disambiguation tool based on a novel clustering-based disambiguation algorithm. To extract relationships among entities, we propose a machine-learning based algorithm that uses a novel stripped dependency tree kernel. We illustrate and evaluate the usefulness of our system on the FEMA National Situation Updates. Daily reports are fetched by FEMARepViz from the FEMA website, segmented into coherent sections and each section is classified into one of several known incident types. We use concept Vista, Google maps and Google earth to visualize the events extracted from the text reports and allow the user to interactively filter the topics, locations, and time-periods of interest to create a visual analytics toolkit that is useful for rapid analysis of events reported in a large set of text documents. Mitra, P. Pan, C.-C. clustering filter geospatial text toolkit visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) geo-temporal visualization geospatial analytics knowledge discovery text processing visual analytics 2007 vast07--4388992 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Stories in GeoTime. A story is a powerful abstraction used by intelligence analysts to conceptualize threats and understand patterns as part of the analytical process. This paper demonstrates a system that detects geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. The GeoTime geo-temporal event visualization tool was augmented with a story system that uses narratives, hypertext linked visualizations, visual annotations, and pattern detection to create an environment for analytic exploration and communication, thereby assisting the analyst in identifying, extracting, arranging and presenting stories within the data The story system lets analysts operate at the story level with higher-level abstractions of data, such as behaviors and events, while staying connected to the evidence. The story system was developed and evaluated in collaboration with analysts. Eccles, R. Harper, R. Kapler, T. Wright, W. collaboration VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) human information interaction narrative pattern detection sensemaking story making storytelling visual analytics 2007 vast07--4388993 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) LAHVA: Linked Animal-Human Health Visual Analytics. Coordinated animal-human health monitoring can provide an early warning system with fewer false alarms for naturally occurring disease outbreaks, as well as biological, chemical and environmental incidents. This monitoring requires the integration and analysis of multi-field, multi-scale and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. To facilitate these investigations, we have created an application to provide a visual analytics framework for analyzing both human emergency room data and veterinary hospital data. Our integrated visual analytic tool links temporally varying geospatial visualization of animal and human patient health information with advanced statistical analysis of these multi-source data. Various statistical analysis techniques have been applied in conjunction with a spatio-temporal viewing window. Such an application provides researchers with the ability to visually search the data for clusters in both a statistical model view and a spatio-temporal view. Our interface provides a factor specification/filtering component to allow exploration of causal factors and spread patterns. In this paper, we will discuss the application of our linked animal-human visual analytics (LAHVA) tool to two specific case studies. The first case study is the effect of seasonal influenza and its correlation with different companion animals (e.g., cats, dogs) syndromes. Here we use data from the Indiana Network for Patient Care (INPC) and Banfield Pet Hospitals in an attempt to determine if there are correlations between respiratory syndromes representing the onset of seasonal influenza in humans and general respiratory syndromes in cats and dogs. Our second case study examines the effect of the release of industrial wastewater in a community through companion animal surveillance. Cleveland, W.S. Ebert, D.S. Glickman, L.T. Grannis, S.J. Jang, Y. Maciejewski, R. Nehme, R.R.V. Ouzzani, M. Tyner, B. Zheng, C. case study geospatial network visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2007 vast07--4388994 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analytics on Mobile Devices for Emergency Response. Using mobile devices for visualization provides a ubiquitous environment for accessing information and effective decision making. These visualizations are critical in satisfying the knowledge needs of operators in areas as diverse as education, business, law enforcement, protective services, medical services, scientific discovery, and homeland security. In this paper, we present an efficient and interactive mobile visual analytic system for increased situational awareness and decision making in emergency response and training situations. Our system provides visual analytics with locational scene data within a simple interface tailored to mobile device capabilities. In particular, we focus on processing and displaying sensor network data for first responders. To verify our system, we have used simulated data of The Station nightclub fire evacuation. Collins, T. Ebert, D.S. Jang, Y. Kim, S.-H. Mellema, A. awareness business education network security visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) emergency response mobile visualization visual analytics 2007 vast07--4388995 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analytics Approach to User-Controlled Evacuation Scheduling. Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster- affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information H. 1.2 [User/Machine Systems]: Human information processing - Visual Analytics; 1.6.9 [Visualization]: information visualization. Andrienko, G. Andrienko, N. Bartling, U. geographic visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) coordinated multiple views geovisualization task-centered design transportation planning vehicle scheduling 2007 vast07--4388996 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Thin Client Visualization. We have developed a Web 2.0 thin client visualization framework called GeoBoosttrade. Our framework focuses on geospatial visualization and using scalable vector graphics (SVG), AJAX, RSS and GeoRSS we have built a complete thin client component set. Our component set provides a rich user experience that is completely browser based. It includes maps, standard business charts, graphs, and time-oriented components. The components are live, interactive, linked, and support real time collaboration. Eick, M.A. Eick, S.G. Fugitt, J. Horst, B. Khailo, M. Lankenau, R.A. business collaboration geospatial VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) JavaScript linked view visual analytics scalable vector graphics visualization components web 2.0 2007 vast07--4388997 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) IMAS: The Interactive Multigenomic Analysis System. This paper introduces a new Visual Analysis tool named IMAS (Interactive Multigenomic Analysis System), which combines common analysis tools such as Glimmer, BLAST, and Clustal-W into a unified Visual Analytic framework. IMAS displays the primary DNA sequence being analyzed by the biologist in a highly interactive, zoomable visual display. The user may analyze the sequence in a number of ways, and visualize these analyses in a coherent, sequence aligned form, with all related analysis products grouped together. This enables the user to rapidly perform analyses of DNA sequences without the need for tedious and error-prone cutting and pasting of sequence data from text files to and from web-based databases and data analysis services, as is now common practice. Dasch, G.A. Eremeeva, M.E. Shaw, C. text VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) bioinformatics visual analytics 2007 vast07--4388998 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Balancing Interactive Data Management of Massive Data with Situational Awareness through Smart Aggregation. Designing a visualization system capable of processing, managing, and presenting massive data sets while maximizing the user's situational awareness (SA) is a challenging, but important, research question in visual analytics. Traditional data management and interactive retrieval approaches have often focused on solving the data overload problem at the expense of the user's SA. This paper discusses various data management strategies and the strengths and limitations of each approach in providing the user with SA. A new data management strategy, coined Smart Aggregation, is presented as a powerful approach to overcome the challenges of both massive data sets and maintaining SA. By combining automatic data aggregation with user-defined controls on what, how, and when data should be aggregated, we present a visualization system that can handle massive amounts of data while affording the user with the best possible SA. This approach ensures that a system is always usable in terms of both system resources and human perceptual resources. We have implemented our Smart Aggregation approach in a visual analytics system called VIAssist (Visual Assistant for Information Assurance Analysis) to facilitate exploration, discovery, and SA in the domain of Information Assurance. Goodall, J.R. Tesone, D.R. awareness visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) data management data retrieval information visualization situational awareness smart aggregation visual analytics 2007 vast07--4388999 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data. Cluster analysis (CA) is a powerful strategy for the exploration of high-dimensional data in the absence of a-priori hypotheses or data classification models, and the results of CA can then be used to form such models. But even though formal models and classification rules may not exist in these data exploration scenarios, domain scientists and experts generally have a vast amount of non-compiled knowledge and intuition that they can bring to bear in this effort. In CA, there are various popular mechanisms to generate the clusters, however, the results from their non- supervised deployment rarely fully agree with this expert knowledge and intuition. To this end, our paper describes a comprehensive and intuitive framework to aid scientists in the derivation of classification hierarchies in CA, using k-means as the overall clustering engine, but allowing them to tune its parameters interactively based on a non-distorted compact visual presentation of the inherent characteristics of the data in high- dimensional space. These include cluster geometry, composition, spatial relations to neighbors, and others. In essence, we provide all the tools necessary for a high-dimensional activity we call cluster sculpting, and the evolving hierarchy can then be viewed in a space-efficient radial dendrogram. We demonstrate our system in the context of the mining and classification of a large collection of millions of data items of aerosol mass spectra, but our framework readily applies to any high-dimensional CA scenario. Han, Y. Imre, D. Mueller, K. Nam, E.J. Zelenyuk, A. cluster clustering hierarchies hierarchy high-dimensional data radial visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) high-dimensional data space and environmental sciences visual analytics visual data mining visualization in earth 2007 vast07--4389000 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Analysis Guided Visual Exploration of Multivariate Data. Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users' exploration in such environments is usually impeded due to several problems: 1) valuable information is hard to discover when too much data is visualized on the screen; 2) Users have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists; 3) their discoveries based on visual exploration alone may lack accuracy; 4) and they have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the nugget management system (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users' visual exploration processes. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, localized data mining techniques are applied to refine the nuggets to best represent the captured patterns in datasets. Lastly, the resulting well-organized nugget pool is used to guide users' exploration. To evaluate the effectiveness of NMS, we integrated NMS into Xmd- vTool, a freeware multivariate visualization system. User studies were performed to compare the users' efficiency and accuracy in finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS. Rundensteiner, E.A. Ward, M.O. Yang, D. clustering data mining VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) analysis guided exploration discovery management visual analytics visual knowledge discovery 2007 vast07--4389001 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Intelligent Visual Analytics Queries. Visualizations of large multi-dimensional data sets, occurring in scientific and commercial applications, often reveal interesting local patterns. Analysts want to identify the causes and impacts of these interesting areas, and they also want to search for similar patterns occurring elsewhere in the data set. In this paper we introduce the Intelligent Visual Analytics Query (IVQuery) concept that combines visual interaction with automated analytical methods to support analysts in discovering the special properties and relations of identified patterns. The idea of IVQuery is to interactively select focus areas in the visualization. Then, according to the characteristics of the selected areas, such as the data dimensions and records, IVQuery employs analytical methods to identify the relationships to other portions of the data set. Finally, IVQuery generates visual representations for analysts to view and refine the results. IVQuery has been applied successfully to different real-world data sets, such as data warehouse performance, product sales, and sever performance analysis, and demonstrates the benefits of this technique over traditional filtering and zooming techniques. The visual analytics query technique can be used with many different types of visual representation. In this paper we show how to use IVQuery with parallel coordinates, visual maps, and scatter plots. Dayal, U. Hao, M.C. Keim, D.A. Morent, D. Schneidewind, J. interaction parallel coordinates visual analytics zooming VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) interactive queries similarity queries visual analytics query 2007 vast07--4389002 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections. The task of building effective representations to visualize and explore collections with moderate to large number of documents is hard. It depends on the evaluation of some distance measure among texts and also on the representation of such relationships in bi- dimensional spaces. In this paper we introduce an alternative approach for building visual maps of documents based on their content similarity, through reconstruction of phylogenetic trees. The tree is capable of representing relationships that allows the user to quickly recover information detected by the similarity metric. For a variety of text collections of different natures we show that we can achieve improved exploration capability and more clear visualization of relationships amongst documents. Cuadros, A.M. Minghim, R. Paulovich, F.V. Telles, G.P. document evaluation text VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) document analysis document visualization multidimensional visualization phylogenetic trees text analytics 2007 vast07--4389003 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Analyzing Large-Scale News Video Databases to Support Knowledge Visualization and Intuitive Retrieval. In this paper, we have developed a novel framework to enable more effective investigation of large-scale news video database via knowledge visualization. To relieve users from the burdensome exploration of well-known and uninteresting knowledge of news reports, a novel interestingness measurement for video news reports is presented to enable users to find news stories of interest at first glance and capture the relevant knowledge in large-scale video news databases efficiently. Our framework takes advantage of both automatic semantic video analysis and human intelligence by integrating with visualization techniques on semantic video retrieval systems. Our techniques on intelligent news video analysis and knowledge discovery have the capacity to enable more effective visualization and exploration of large-scale news video collections. In addition, news video visualization and exploration can provide valuable feedback to improve our techniques for intelligent news video analysis and knowledge discovery. Fan, J. Luo, H. Ribarsky, W. Satoh, S. Yang, J. database VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) knowledge discovery knowledge visualization semantic video classification 2007 vast07--4389004 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Literature Fingerprinting: A New Method for Visual Literary Analysis. In computer-based literary analysis different types of features are used to characterize a text. Usually, only a single feature value or vector is calculated for the whole text. In this paper, we combine automatic literature analysis methods with an effective visualization technique to analyze the behavior of the feature values across the text. For an interactive visual analysis, we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint. The feature values may be calculated on different hierarchy levels, allowing the analysis to be done on different resolution levels. A case study shows several successful applications of our new method to known literature problems and demonstrates the advantage of our new visual literature fingerprinting. Keim, D.A. Oelke, D. case study hierarchy text VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) literature fingerprinting visual analytics visual literature analysis 2007 vast07--4389005 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) NewsLab: Exploratory Broadcast News Video Analysis. In this paper, we introduce NewsLab, an exploratory visualization approach for the analysis of large scale broadcast news video collections containing many thousands of news stories over extended periods of time. A river metaphor is used to depict the thematic changes of the news over time. An interactive lens metaphor allows the playback of fine-grained video segments selected through the river overview. Multi-resolution navigation is supported via a hierarchical time structure as well as a hierarchical theme structure. Themes can be explored hierarchically according to their thematic structure, or in an unstructured fashion using various ranking criteria. A rich set of interactions such as filtering, drill-down/roll-up navigation, history animation, and keyword based search are also provided. Our case studies show how this set of tools can be used to find emerging topics in the news, compare different broadcasters, or mine the news for topics of interest. Ghoniem, M. Luo, D. Ribarsky, W. Yang, J. animation history navigation overview VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) animation broadcast video analysis clustering comparative analysis large data exploration time filtering 2007 vast07--4389006 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Jigsaw: Supporting Investigative Analysis through Interactive Visualization. Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents. Görg, C. Liu, Z. Singhal, K. Stasko, J. coordinated views document text VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) information visualization intelligence analysis investigative analysis multiple views visual analytics 2007 vast07--4389007 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) SpiralView: Towards Security Policies Assessment through Visual Correlation of Network Resources with Evolution of Alarms. This article presents SpiralView, a visualization tool for helping system administrators to assess network policies. The tool is meant to be a complementary support to the routine activity of network monitoring, enabling a retrospective view on the alarms generated during and extended period of time. The tool permits to reason about how alarms distribute over time and how they correlate with network resources (e.g., users, IPs, applications, etc.), supporting the analysts in understanding how the network evolves and thus in devising new security policies for the future. The spiral visualization plots alarms in time, and, coupled with interactive bar charts and a users/applications graph view, is used to present network data and perform queries. The user is able to segment the data in meaningful subsets, zoom on specific related information, and inspect for relationships between alarms, users, and applications. In designing the visualizations and their interaction, and through tests with security experts, several ameliorations over the standard techniques have been provided. Bertini, E. Hertzog, P. Lalanne, D. graph interaction network security zoom VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) data exploration intrusion detection network security visualization 2007 vast07--4389008 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Session Viewer: Visual Exploratory Analysis of Web Session Logs. Large-scale session log analysis typically includes statistical methods and detailed log examinations. While both methods have merits, statistical methods can miss previously unknown sub- populations in the data and detailed analyses may have selection biases. We therefore built Session Viewer, a visualization tool to facilitate and bridge between statistical and detailed analyses. Taking a multiple-coordinated view approach, Session Viewer shows multiple session populations at the Aggregate, Multiple, and Detail data levels to support different analysis styles. To bridge between the statistical and the detailed analysis levels, Session Viewer provides fluid traversal between data levels and side-by-side comparison at all data levels. We describe an analysis of a large-scale web usage study to demonstrate the use of Session Viewer, where we quantified the importance of grouping sessions based on task type. Lam, H. Munzner, T. Russell, D. Tang, D. VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) information visualization visual exploratory data analysis web session log analysis 2007 vast07--4389009 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions. Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique which extracts accounts that show similar transaction patterns. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors. Chang, R. Ghoniem, M. Kern, D. Kosara, R. Ribarsky, W. Sudjianto, A. Suma, E. Yang, J. Ziemkiewicz, C. categorical collaboration financial VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) categorial time-varying data financial data visualization fraud detection 2007 vast07--4389010 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Us vs. Them: Understanding Social Dynamics in Wikipedia with Revert Graph Visualizations. Wikipedia is a wiki-based encyclopedia that has become one of the most popular collaborative on-line knowledge systems. As in any large collaborative system, as Wikipedia has grown, conflicts and coordination costs have increased dramatically. Visual analytic tools provide a mechanism for addressing these issues by enabling users to more quickly and effectively make sense of the status of a collaborative environment. In this paper we describe a model for identifying patterns of conflicts in Wikipedia articles. The model relies on users' editing history and the relationships between user edits, especially revisions that void previous edits, known as "reverts". Based on this model, we constructed Revert Graph, a tool that visualizes the overall conflict patterns between groups of users. It enables visual analysis of opinion groups and rapid interactive exploration of those relationships via detail drill- downs. We present user patterns and case studies that show the effectiveness of these techniques, and discuss how they could generalize to other systems. Chi, E.H. Kittur, A. Pendleton, B.A. Suh, B. graph history social VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Wikipedia collaboration graph revert user model visualization wiki 2007 vast07--4389011 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Design Considerations for Collaborative Visual Analytics. Information visualization leverages the human visual system to support the process of sensemaking, in which information is collected, organized, and analyzed to generate knowledge and inform action. Though most research to date assumes a single-user focus on perceptual and cognitive processes, in practice, sensemaking is often a social process involving parallelization of effort, discussion, and consensus building. This suggests that to fully support sensemaking, interactive visualization should also support social interaction. However, the most appropriate collaboration mechanisms for supporting this interaction are not immediately clear. In this article, we present design considerations for asynchronous collaboration in visual analysis environments, highlighting issues of work parallelization, communication, and social organization. These considerations provide a guide for the design and evaluation of collaborative visualization systems. Agrawala, M. Heer, J. collaboration evaluation interaction sensemaking social visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) analysis collaboration computer-supported cooperative work design visualization 2007 vast07--4389012 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual Analysis of Controversy in User-generated Encyclopedias. Wikipedia is a large and rapidly growing Web-based collaborative authoring environment, where anyone on the Internet can create, modify, and delete pages about encyclopedic topics. A remarkable property of some Wikipedia pages is that they are written by up to thousands of authors who may have contradicting opinions. In this paper we show that a visual analysis of the "who revises whom"- network gives deep insight into controversies. We propose a set of analysis and visualization techniques that reveal the dominant authors of a page, the roles they play, and the alters they confront. Thereby we provide tools to understand how Wikipedia authors collaborate in the presence of controversy. Brandes, U. Lerner, J. insight network VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) Wikipedia controversy social network analysis 2007 vast07--4389013 10/30/2007 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data. Supporting visual analytics of multiple large-scale multidimensional datasets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such datasets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a dataset displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to outsiders. Towards this end, the DataMeadow has a direct manipulation interface for selection, filtering, and creation of sets, subsets, and data dependencies using both simple and complex mouse gestures. We have evaluated our system using a qualitative expert review involving two researchers working in the area. Results from this review are favorable for our new method. Elmqvist, N. Stasko, J. Tsigas, P. dynamic query history interaction visual analytics VAST 2007 IEEE Symposium on Visual Analytics Science and Technology (VAST) dynamic query iterative analysis multivariate data parallel coordinates small multiples starplot visual analytics 2007 vast08--4677370 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Multidimensional visual analysis using cross-filtered views. Analysis of multidimensional data often requires careful examination of relationships across dimensions. Coordinated multiple view approaches have become commonplace in visual analysis tools because they directly support expression of complex multidimensional queries using simple interactions. However, generating such tools remains difficult because of the need to map domain-specific data structures and semantics into the idiosyncratic combinations of interdependent data and visual abstractions needed to reveal particular patterns and distributions in cross-dimensional relationships. This paper describes: (1) a method for interactively expressing sequences of multidimensional set queries by cross-filtering data values across pairs of views, and (2) design strategies for constructing coordinated multiple view interfaces for cross-filtered visual analysis of multidimensional data sets. Using examples of cross-filtered visualizations of data from several different domains, we describe how cross-filtering can be modularized and reused across designs, flexibly customized with respect to data types across multiple dimensions, and incorporated into more wide-ranging multiple view designs. The demonstrated analytic utility of these examples suggest that cross-filtering is a suitable design pattern for instantiation in a wide variety of visual analysis tools. Weaver, C. VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677350 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual cluster analysis of trajectory data with interactive Kohonen Maps. Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results. Bernard, J. Kohlhammer, J. Schreck, T. Tekusova, T. cluster clustering visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677351 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Crystal structures classifier for an evolutionary algorithm structure predictor. USPEX is a crystal structure predictor based on an evolutionary algorithm. Every USPEX run produces hundreds or thousands of crystal structures, some of which may be identical. To ease the extraction of unique and potentially interesting structures we applied usual high-dimensional classification concepts to the unusual field of crystallography. We experimented with various crystal structure descriptors, distinct distance measures and tried different clustering methods to identify groups of similar structures. These methods are already applied in combinatorial chemistry to organic molecules for a different goal and in somewhat different forms, but are not widely used for crystal structures classification. We adopted a visual design and validation method in the development of a library (CrystalFp) and an end-user application to select and validate method choices, to gain usersˇŻ acceptance and to tap into their domain expertise. The use of the classifier has already accelerated the analysis of USPEX output by at least one order of magnitude, promoting some new crystallographic insight and discovery. Furthermore the visual display of key algorithm indicators has led to diverse, unexpected discoveries that will improve the USPEX algorithms. Oganov, A.R. Valle, M. clustering insight VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677352 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Model-driven Visual Analytics. We describe a Visual Analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use Logic Programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the high-dimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains. Garg, S. Mueller, K. Nam, J.E. Ramakrishnan, I.V. machine learning visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677353 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Using visual analytics to maintain situation awareness in astrophysics. We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists needing to analyze heterogeneous, complex data under time pressure, and then make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in use for over eighteen months by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture, and the results of an informal usability evaluation of the production system by domain experts in the context of EndsleyˇŻs three levels of situation awareness [1]. Aldering, G.S. Aragon, C.R. Poon, S.S. Quimby, R. Thomas, R.C. awareness collaboration evaluation usability visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677354 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Understanding syndromic hotspots - a visual analytics approach. When analyzing syndromic surveillance data, health care officials look for areas with unusually high cases of syndromes. Unfortunately, many outbreaks are difficult to detect because their signal is obscured by the statistical noise. Consequently, many detection algorithms have a high false positive rate. While many false alerts can be easily filtered by trained epidemiologists, others require health officials to drill down into the data, analyzing specific segments of the population and historical trends over time and space. Furthermore, the ability to accurately recognize meaningful patterns in the data becomes more challenging as these data sources increase in volume and complexity. To facilitate more accurate and efficient event detection, we have created a visual analytics tool that provides analysts with linked geo-spatiotemporal and statistical analytic views. We model syndromic hotspots by applying a kernel density estimation on the population sample. When an analyst selects a syndromic hotspot, temporal statistical graphs of the hotspot are created. Similarly, regions in the statistical plots may be selected to generate geospatial features specific to the current time period. Demographic filtering can then be combined to determine if certain populations are more affected than others. These tools allow analysts to perform real-time hypothesis testing and evaluation. Abusalah, A. Cleveland, W.S. Ebert, D.S. Grannis, S.J. Hafen, R. Maciejewski, R. Ouzzani, M. Rudolph, S. Wade, M. Yakout, M. evaluation geospatial visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677355 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Configurable Spaces: Temporal analysis in diagrammatic contexts. Social network graphs, concept maps, and process charts are examples of diagrammatic representations employed by intelligence analysts to understand complex systems. Unfortunately, these 2D representations currently do not easily convey the flow, sequence, tempo and other important dynamic behaviors within these systems. In this paper we present Configurable Spaces, a novel analytical method for visualizing patterns of activity over time in complex diagrammatically-represented systems. Configurable Spaces extends GeoTimeˇŻs X, Y, T coordinate workspace space for temporal analysis to any arbitrary diagrammatic work space by replacing a geographic map with a diagram. Eccles, R. Harper, R. Kapler, T. Wright, W. geographic network social VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677356 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Spatio-temporal aggregation for visual analysis of movements. Data about movements of various objects are collected in growing amounts by means of current tracking technologies. Traditional approaches to visualization and interactive exploration of movement data cannot cope with data of such sizes. In this research paper we investigate the ways of using aggregation for visual analysis of movement data. We define aggregation methods suitable for movement data and find visualization and interaction techniques to represent results of aggregations and enable comprehensive exploration of the data. We consider two possible views of movement, traffic-oriented and trajectory-oriented. Each view requires different methods of analysis and of data aggregation. We illustrate our argument with example data resulting from tracking multiple cars in Milan and example analysis tasks from the domain of city traffic management. Andrienko, G. Andrienko, N. interaction VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677357 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Maintaining interactivity while exploring massive time series. The speed of data retrieval qualitatively affects how analysts visually explore and analyze their data. To ensure smooth interactions in massive time series datasets, one needs to address the challenges of computing ad hoc queries, distributing query load, and hiding system latency. In this paper, we present ATLAS, a visualization tool for temporal data that addresses these issues using a combination of high performance database technology, predictive caching, and level of detail management. We demonstrate ATLAS using commodity hardware on a network traffic dataset of more than a billion records. Chan, S.M. Gerth, J. Hanrahan, P. Xiao, L. database hardware network time series VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677358 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Collaborative synthesis of visual analytic results. Visual analytic tools allow analysts to generate large collections of useful analytical results. We anticipate that analysts in most real world situations will draw from these collections when working together to solve complicated problems. This indicates a need to understand how users synthesize multiple collections of results. This paper reports the results of collaborative synthesis experiments conducted with expert geographers and disease biologists. Ten participants were worked in pairs to complete a simulated real-world synthesis task using artifacts printed on cards on a large, paper-covered workspace. Experiment results indicate that groups use a number of different approaches to collaborative synthesis, and that they employ a variety of organizational metaphors to structure their information. It is further evident that establishing common ground and role assignment are critical aspects of collaborative synthesis. We conclude with a set of general design guidelines for collaborative synthesis support tools. Robinson, A.C. experiment VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677359 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual evaluation of text features for document summarization and analysis. Thanks to the web-related and other advanced technologies, textual information is increasingly being stored in digital form and posted online. Automatic methods to analyze such textual information are becoming inevitable. Many of those methods are based on quantitative text features. Analysts face the challenge to choose the most appropriate features for their tasks. This requires effective approaches for evaluation and feature-engineering. Bak, P. Danon, G. Keim, D.A. Last, M. Oelke, D. document evaluation text VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677360 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Evaluating the relationship between user interaction and financial visual analysis. It has been widely accepted that interactive visualization techniques enable users to more effectively form hypotheses and identify areas for more detailed investigation. There have been numerous empirical user studies testing the effectiveness of specific visual analytical tools. However, there has been limited effort in connecting a userˇŻs interaction with his reasoning for the purpose of extracting the relationship between the two. In this paper, we present an approach for capturing and analyzing user interactions in a financial visual analytical tool and describe an exploratory user study that examines these interaction strategies. To achieve this goal, we created two visual tools to analyze raw interaction data captured during the user session. The results of this study demonstrate one possible strategy for understanding the relationship between interaction and reasoning both operationally and strategically. Chang, R. Dou, W. Jeong, D.H. Lipford, H.R. Ribarsky, W. Stukes, F. financial interaction user study VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677361 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual analytics for complex concepts using a human cognition model. As the information being visualized and the process of understanding that information both become increasingly complex, it is necessary to develop new visualization approaches that facilitate the flow of human reasoning. In this paper, we endeavor to push visualization design a step beyond current user models by discussing a modeling framework of human ˇ°higher cognition.ˇ± Based on this cognition model, we present design guidelines for the development of visual interfaces designed to maximize the complementary cognitive strengths of both human and computer. Some of these principles are already being reflected in the better visual analytics designs, while others have not yet been applied or fully applied. But none of the guidelines have explained the deeper rationale that the model provides. Lastly, we discuss and assess these visual analytics guidelines through the evaluation of several visualization examples. Fisher, B. Green, T.M. Ribarsky, W. cognition evaluation visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677362 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Entity-based collaboration tools for intelligence analysis. Software tools that make it easier for analysts to collaborate as a natural part of their work will lead to better analysis that is informed by more perspectives. We are interested to know if software tools can be designed that support collaboration even as they allow analysts to find documents and organize information (including evidence, schemas, and hypotheses). We have modified the Entity Workspace system, described previously, to test such designs. We have evaluated the resulting design in both a laboratory study and a study where it is situated with an analysis team. In both cases, effects on collaboration appear to be positive. Key aspects of the design include an evidence notebook optimized for organizing entities (rather than text characters), information structures that can be collapsed and expanded, visualization of evidence that emphasizes events and documents (rather than emphasizing the entity graph), and a notification system that finds entities of mutual interest to multiple analyst. Bier, E.A. Bodnar, J.W. Card, S.K. collaboration graph intelligence analysis text VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677363 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Applied visual analytics for economic decision-making. This paper introduces the application of visual analytics techniques as a novel approach for improving economic decision making. Particularly, we focus on two known problems where subjectsˇŻ behavior consistently deviates from the optimal, the WinnerˇŻs and LoserˇŻs Curse. According to economists, subjects fail to recognize the profit-maximizing decision strategy in both the WinnerˇŻs and LoserˇŻs curse because they are unable to properly consider all the available information. As such, we have created a visual analytics tool to aid subjects in decision making under the Acquiring a Company framework common in many economic experiments. We demonstrate the added value of visual analytics in the decision making process through a series of user studies comparing standard visualization methods with interactive visual analytics techniques. Our work presents not only a basis for development and evaluation of economic visual analytic research, but also empirical evidence demonstrating the added value of applying visual analytics to general decision making tasks. Ebert, D.S. Maciejewski, R. Savikhin, A. evaluation visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677364 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Narratives: A visualization to track narrative events as they develop. Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories. Fisher, D. Hoff, A. Hurst, M. Robertson, G. clustering social text VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677365 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Characterizing usersˇŻ visual analytic activity for insight provenance. Insight provenance?a historical record of the process and rationale by which an insight is derived?is an essential requirement in many visual analytics applications. While work in this area has relied on either manually recorded provenance (e.g., user notes) or automatically recorded event-based insight provenance (e.g., clicks, drags, and key-presses), both approaches have fundamental limitations. Our aim is to develop a new approach that combines the benefits of both approaches while avoiding their deficiencies. Toward this goal, we characterize usersˇŻ visual analytic activity at multiple levels of granularity. Moreover, we identify a critical level of abstraction, Actions, that can be used to represent visual analytic activity with a set of general but semantically meaningful behavior types. In turn, the action types can be used as the semantic building blocks for insight provenance. We present a catalog of common actions identified through observations of several different visual analytic systems. In addition, we define a taxonomy to categorize actions into three major classes based on their semantic intent. The concept of actions has been integrated into our labˇŻs prototype visual analytic system, HARVEST, as the basis for its insight provenance capabilities. Gotz, D. Zhou, M.X. insight taxonomy visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677366 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) The Scalable Reasoning System: Lightweight visualization for distributed analytics. A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the Scalable Reasoning System (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ˇ°lightweightˇ± approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization. Baddeley, B. Best, D. Bruce, J. Franklin, L. May, R. Pike, W.A. Rice, D.M. Riensche, R. Younkin, K. coordinated views visual analytics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677367 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Generating hypotheses of trends in high-dimensional data skeletons. We seek an information-revealing representation for high-dimensional data distributions that may contain local trends in certain subspaces. Examples are data that have continuous support in simple shapes with identifiable branches. Such data can be represented by a graph that consists of segments of locally fit principal curves or surfaces summarizing each identifiable branch. We describe a new algorithm to find the optimal paths through such a principal graph. The paths are optimal in the sense that they represent the longest smooth trends through the data set, and jointly they cover the data set entirely with minimum overlap. The algorithm is suitable for hypothesizing trends in high-dimensional data, and can assist exploratory data analysis and visualization. Ho, T.K. Pokharkar, S. Reddy, C.K. graph high-dimensional data VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677368 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Multivariate visual explanation for high dimensional datasets. Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. Barlowe, S. Jacobs, D. Liu, Y. Yang, J. Zhang, T. dimension reduction user study VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast08--4677369 10/21/2008 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual mining of multimedia data for social and behavioral studies. With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, etc.) has been collected in research laboratories in various scientific disciplines, particularly in social and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge since state-of-the-art data mining techniques can most often only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this, our approach is to take advantages of both the power of human perception system and the power of computational algorithms. More specifically, we propose an approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) A smooth interface between visualization and data mining. The new analysis results can be automatically loaded into our visualization tool. (2) A flexible tool to explore and query temporal data derived from raw multimedia data. We represent temporal data into two forms - continuous variables and event variables. We have developed various ways to visualize both temporal correlations and statistics of multiple variables with the same type, and conditional and high-order statistics between continuous and event variables. (3) A seamless interface between raw multimedia data and derived data. Our visualization tool allows users to explore, compare, and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data. We de- - monstrate various functions in our visualization program using a set of multimedia data including video, audio and motion tracking data. Huang, W. Park, I. Smith, T. Yu, C. Zhong, Y. data mining perception social statistics VAST 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2008 vast09--5333564 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Iterative integration of visual insights during patent search and analysis. Patents are an important economic factor in todays globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With `PatViz', a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems. Bosch, H. Ertl, T. Giereth, M. Koch, S. visual analytics VAST PatViz complex query languages economic factor globalized markets information analysis information retrieval interactive analysis iterative integration iterative query refinement patent document retrieval patent information analysis patent information retrieval patent material patent search patents visual analytics system visual insight visual query module visual query representation 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) information visualization multiple coordinated views patent retrieval visual analytics 2009 vast09--5333920 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) FinVis: Applied visual analytics for personal financial planning. FinVis is a visual analytics tool that allows the non-expert casual user to interpret the return, risk and correlation aspects of financial data and make personal finance decisions. This interactive exploratory tool helps the casual decision-maker quickly choose between various financial portfolio options and view possible outcomes. FinVis allows for exploration of inter-temporal data to analyze outcomes of short-term or long-term investment decisions. FinVis helps the user overcome cognitive limitations and understand the impact of correlation between financial instruments in order to reap the benefits of portfolio diversification. Because this software is accessible by non-expert users, decision-makers from the general population can benefit greatly from using FinVis in practical applications. We quantify the value of FinVis using experimental economics methods and find that subjects using the FinVis software make better financial portfolio decisions as compared to subjects using a tabular version with the same information. We also find that FinVis engages the user, which results in greater exploration of the dataset and increased learning as compared to a tabular display. Further, participants using FinVis reported increased confidence in financial decision-making and noted that they were likely to use this tool in practical application. Ebert, D.S. Rudolph, S. Savikhin, A. financial visual analytics VAST 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) casual information visualization economic decision-making personal finance visual analytics visualization of risk 2009 vast09--5333919 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual opinion analysis of customer feedback data. Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilized - even though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze customer comments and ratings to determine the positive and negative opinions expressed by the customers. First, we introduce a new discrimination-based technique to automatically extract the terms that are the subject of the positive or negative opinion (such as price or customer service) and that are frequently commented on. Second, we derive a Reverse-Distance-Weighting method to map the attributes to the related positive and negative opinions in the text. Third, the resulting high-dimensional feature vectors are visualized in a new summary representation that provides a quick overview. We also cluster the reviews according to the similarity of the comments. Special thumbnails are used to provide insight into the composition of the clusters and their relationship. In addition, an interactive circular correlation map is provided to allow analysts to detect the relationships of the comments to other important attributes and the scores. We have applied these techniques to customer comments from real-world online stores and product reviews from web sites to identify the strength and problems of different products and services, and show the potential of our technique. Dayal, U. Hao, M.C. Haug, L.-E. Janetzko, H. Keim, D.A. Oelke, D. Rohrdantz, C. business cluster insight overview text VAST Internet Web sites customer feedback data customer satisfaction data visualisation discrimination-based technique high-dimensional feature vectors interactive circular correlation map product reviews real-world online stores reverse-distance-weighting method visual opinion analysis 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) attribute extraction visual document analysis visual opinion analysis visual sentiment analysis 2009 vast09--5333911 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) SpRay: A visual analytics approach for gene expression data. We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by micro-array experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications. Bartz, D. Dietzsch, J. Heinrich, J. Nieselt, K. bioinformatics parallel coordinates visual analytics VAST SpRay bioinformatics bioinformatics applications genetics high-dimensional datasets multidimensional gene expression datasets parallel coordinates statistical analysis statistical methods visual analytics approach visual exploration 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) bioinformatics gene expression experiments large-scale microarray microarray data visual analytics 2009 vast09--5333917 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Using projection and 2D plots to visually reveal genetic mechanisms of complex human disorders. Gene mapping is a statistical method used to localize human disease genes to particular regions of the human genome. When performing such analysis, a genetic likelihood space is generated and sampled, which results in a multidimensional scalar field. Researchers are interested in exploring this likelihood space through the use of visualization. Previous efforts at visualizing this space, though, were slow and cumbersome, only showing a small portion of the space at a time, thus requiring the user to keep a mental picture of several views. We have developed a new technique that displays much more data at once by projecting the multidimensional data into several 2D plots. One plot is created for each parameter that shows the change along that parameter. A radial projection is used to create another plot that provides an overview of the high dimensional surface from the perspective of a single point. Linking and brushing between all the plots are used to determine relationships between parameters. We demonstrate our techniques on real world autism data, showing how to visually examine features of the high dimensional space. Nouanesengsy, B. Seok, S.-C. Shen, H.-W. Vieland, V.J. brushing overview radial VAST 2D plots complex human disorders data visualisation diseases gene mapping genetic likelihood space genetic mechanism visualization genetics human disease genes human genome medical computing multidimensional data projection multidimensional scalar field radial projection statistical analysis statistical method 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) LD analysis PPL PPLD autism linkage analysis linkage disequilibrium multidimensional data posterior probability of linkage visualization 2009 vast09--5333895 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) MassVis: Visual analysis of protein complexes using mass spectrometry. Protein complexes are formed when two or more proteins non-covalently interact to form a larger three dimensional structure with specific biological function. Understanding the composition of such complexes is vital to understanding cell biology at the molecular level. MassVis is a visual analysis tool designed to assist the interpretation of data from a new workflow for detecting the composition of such protein complexes in biological samples. The data generated by the laboratory workflow naturally lends itself to a scatter plot visualization. However, characteristics of this data give rise to some unique aspects not typical of a standard scatter plot. We are able to take the output from tandem mass spectrometry and render the data in such a way that it mimics more traditional two-dimensional gel techniques and at the same time reveals the correlated behavior indicative of protein complexes. By computationally measuring these correlated patterns in the data, membership in putative complexes can be inferred. User interactions are provided to support both an interactive discovery mode as well as an unsupervised clustering of likely complexes. The specific analysis tasks led us to design a unique arrangement of item selection and coordinated detail views in order to simultaneously view different aspects of the selected item. Dejgaard, K. Kincaid, R. clustering VAST MassVis biology computing cell biology cellular biophysics mass spectrometry mass spectroscopy pattern clustering protein complexes proteins putative complexes tandem mass spectrometry two-dimensional gel techniques unsupervised clustering user interaction visual analysis tool 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) correlation analysis information visualization interactome mass spectrometry proteomics visual analysis 2009 vast09--5333893 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual analysis of graphs with multiple connected components. In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the self-organizing-map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder network analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types. Gorner, M. Schreck, T. von Landesberger, T. cluster clustering graph insight network VAST SOM algorithm data visualisation directed graphs graph clustering interactive feature selection interactive systems interactive visualization analysis mathematics computing multiple connected component pattern clustering self-organising feature maps self-organizing-map algorithm shareholder network analysis problem task-tailored data topology descriptor weighted directed graph data set 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5333880 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) A multi-level middle-out cross-zooming approach for large graph analytics. This paper presents a working graph analytics model that embraces the strengths of the traditional top-down and bottom-up approaches with a resilient crossover concept to exploit the vast middle-ground information overlooked by the two extreme analytical approaches. Our graph analytics model is co-developed by users and researchers, who carefully studied the functional requirements that reflect the critical thinking and interaction pattern of a real-life intelligence analyst. To evaluate the model, we implement a system prototype, known as GreenHornet, which allows our analysts to test the theory in practice, identify the technological and usage-related gaps in the model, and then adapt the new technology in their work space. The paper describes the implementation of GreenHornet and compares its strengths and weaknesses against the other prevailing models and tools. Cook, K.A. Foote, H. Mackey, P. Rohrer, R.M. Whiting, M.A. Wong, P.C. graph interaction theory zooming VAST GreenHornet system prototype bottom-up approach data visualisation human computer interaction human-visualization interface information visualization multilevel middle-out cross-zooming approach top-down approach working graph analytics model 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) graph analytics information visualization 2009 vast09--5333878 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study. Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and we compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations for metrics and techniques for evaluating other visual analytics investigative analysis tools. Görg, C. Kang, Y. Stasko, J. case study evaluation insight intelligence analysis metrics sensemaking visual analytics VAST Jigsaw system data visualisation interactive systems interactive visualization investigative analysis investigative sensemaking exercise simulated intelligence analysis task visual analytics system 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5333020 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Capturing and supporting the analysis process. Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses. Chen, V. Dill, J. Dunsmuir, D. Kadivar, N. Lee, E. Qian, C. Shaw, C. Woodbury, R. document graph history navigation visual analytics VAST CzSaw-visual analytics system authoring languages data visualisation dependency graph document collection graph theory script language scripting-driven propagation system user history navigation channel visual analytics tool visual representation visualization 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) analysis process sensemaking visual analytics visual history 2009 vast09--5333023 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Connecting the dots in visual analysis. During visual analysis, users must often connect insights discovered at various points of time. This process is often called ldquoconnecting the dots.rdquo When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users' past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a Web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach. Gotz, D. Lu, J. Shrinivasan, Y.B. case study VAST Harvest system Web based visual analytic system collaborative analysis task connecting-the-dots process context based retrieval algorithm datasets exploration information filtering information retrieval recommendation feature visual analysis 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5333245 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) VAST contest dataset use in education. The IEEE Visual Analytics Science and Technology (VAST) Symposium has held a contest each year since its inception in 2006. These events are designed to provide visual analytics researchers and developers with analytic challenges similar to those encountered by professional information analysts. The VAST contest has had an extended life outside of the symposium, however, as materials are being used in universities and other educational settings, either to help teachers of visual analytics-related classes or for student projects. We describe how we develop VAST contest datasets that results in products that can be used in different settings and review some specific examples of the adoption of the VAST contest materials in the classroom. The examples are drawn from graduate and undergraduate courses at Virginia Tech and from the Visual Analytics ldquoSummer Camprdquo run by the National Visualization and Analytics Center in 2008. We finish with a brief discussion on evaluation metrics for education. Endert, A. Haack, J. North, C. Scholtz, J. Thomas, J. Varley, C. Whiting, M.A. education evaluation metrics visual analytics VAST IEEE visual analytics science and technology VAST data visualisation education educational technology evaluation metrics information analysis information analysts 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) education evaluation synthetic data 2009 vast09--5333248 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) What's being said near ?Martha?? Exploring name entities in literary text collections. A common task in literary analysis is to study characters in a novel or collection. Automatic entity extraction, text analysis and effective user interfaces facilitate character analysis. Using our interface, called POSvis, the scholar uses word clouds and self-organizing graphs to review vocabulary, to filter by part of speech, and to explore the network of characters located near characters under review. Further, visualizations show word usages within an analysis window (i.e. a book chapter), which can be compared with a reference window (i.e. the whole book). We describe the interface and report on an early case study with a humanities scholar. Clement, T. Kumar, A. Plaisant, C. Vuillemot, R. case study filter network text VAST POSvis automatic entity extraction character analysis data visualisation humanities scholar information filtering linguistics literary analysis literary text collection name entity part-of-speech filtering self-organizing graph text analysis user interface user interfaces vocabulary word clouds word usage 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) design experimentation human factors visual analytics 2009 vast09--5333437 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Describing story evolution from dynamic information streams. Sources of streaming information, such as news syndicates, publish information continuously. Information portals and news aggregators list the latest information from around the world enabling information consumers to easily identify events in the past 24 hours. The volume and velocity of these streams causes information from prior days to quickly vanish despite its utility in providing an informative context for interpreting new information. Few capabilities exist to support an individual attempting to identify or understand trends and changes from streaming information over time. The burden of retaining prior information and integrating with the new is left to the skills, determination, and discipline of each individual. In this paper we present a visual analytics system for linking essential content from information streams over time into dynamic stories that develop and change over multiple days. We describe particular challenges to the analysis of streaming information and present a fundamental visual representation for showing story change and evolution over time. Butner, S. Cowley, W. Gregory, M. Rose, S. Walker, J. visual analytics VAST data visualisation dynamic information streaming fundamental visual representation information analysis information filtering information portals portals visual analytics system 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5333443 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Parallel Tag Clouds to explore and analyze faceted text corpora. Do court cases differ from place to place? What kind of picture do we get by looking at a country's collection of law cases? We introduce parallel tag clouds: a new way to visualize differences amongst facets of very large metadata-rich text corpora. We have pointed parallel tag clouds at a collection of over 600,000 US Circuit Court decisions spanning a period of 50 years and have discovered regional as well as linguistic differences between courts. The visualization technique combines graphical elements from parallel coordinates and traditional tag clouds to provide rich overviews of a document collection while acting as an entry point for exploration of individual texts. We augment basic parallel tag clouds with a details-in-context display and an option to visualize changes over a second facet of the data, such as time. We also address text mining challenges such as selecting the best words to visualize, and how to do so in reasonable time periods to maintain interactivity. Collins, C. ViĂ©gas, F.B. Wattenberg, M. document parallel coordinates text VAST data mining data visualisation faceted text corpora graphical element parallel coordinate parallel tag cloud text analysis text mining 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) corpus visualization information retrieval tag cloud text mining text visualization 2009 vast09--5333428 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) LSAView: A tool for visual exploration of latent semantic modeling. Latent Semantic Analysis (LSA) is a commonly-used method for automated processing, modeling, and analysis of unstructured text data. One of the biggest challenges in using LSA is determining the appropriate model parameters to use for different data domains and types of analyses. Although automated methods have been developed to make rank and scaling parameter choices, these approaches often make choices with respect to noise in the data, without an understanding of how those choices impact analysis and problem solving. Further, no tools currently exist to explore the relationships between an LSA model and analysis methods. Our work focuses on how parameter choices impact analysis and problem solving. In this paper, we present LSAView, a system for interactively exploring parameter choices for LSA models. We illustrate the use of LSAView's small multiple views, linked matrix-graph views, and data views to analyze parameter selection and application in the context of graph layout and clustering. Crossno, P.J. Dunlavy, D.M. Shead, T.M. clustering graph graph layout matrix multiple views text VAST LSA model LSAView automated processing data visualisation impact analysis latent semantic analysis latent semantic modeling linked matrix-graph views problem solving rank parameter scaling parameter unstructured text data visual exploration 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5333431 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Model space visualization for multivariate linear trend discovery. Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenomena. However, current widely used multivariate visualization techniques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such linear relationships intuitively, especially when more than 3 variables are involved or multiple trends coexist in the dataset. We present a novel multivariate model parameter space visualization system that helps analysts discover single and multiple linear patterns and extract subsets of data that fit a model well. Using this system, analysts are able to explore and navigate in model parameter space, interactively select and tune patterns, and refine the model for accuracy using computational techniques. We build connections between model space and data space visually, allowing analysts to employ their domain knowledge during exploration to better interpret the patterns they discover and their validity. Case studies with real datasets are used to investigate the effectiveness of the visualizations. Guo, Z. Rundensteiner, E.A. Ward, M.O. parallel coordinates scatterplot VAST data mining data space data visualisation domain knowledge linear pattern discovery model space visualization multivariate linear trend discovery 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) knowledge discovery model space visualization multivariate linear model construction visual analysis 2009 vast09--5332629 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Two-stage framework for visualization of clustered high dimensional data. In this paper, we discuss dimension reduction methods for 2D visualization of high dimensional clustered data. We propose a twostage framework for visualizing such data based on dimension reduction methods. In the first stage, we obtain the reduced dimensional data by applying a supervised dimension reduction method such as linear discriminant analysis which preserves the original cluster structure in terms of its criteria. The resulting optimal reduced dimension depends on the optimization criteria and is often larger than 2. In the second stage, the dimension is further reduced to 2 for visualization purposes by another dimension reduction method such as principal component analysis. The role of the second-stage is to minimize the loss of information due to reducing the dimension all the way to 2. Using this framework, we propose several two-stage methods, and present their theoretical characteristics as well as experimental comparisons on both artificial and real-world text data sets. Bohn, S. Choo, J. Park, H. cluster dimension reduction text VAST visual analytics 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2D projection clustered data dimension reduction generalized singular value decomposition linear discriminant analysis orthogonal centroid method principal component analysis regularization 2009 vast09--5332628 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets. Albuquerque, G. Eisemann, M. Keim, D.A. Magnork, M. Schneidewind, J. Tatu, A. Theisel, H. high-dimensional data parallel coordinates VAST visual analytics 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5332611 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) A framework for uncertainty-aware visual analytics. Visual analytics has become an important tool for gaining insight on large and complex collections of data. Numerous statistical tools and data transformations, such as projections, binning and clustering, have been coupled with visualization to help analysts understand data better and faster. However, data is inherently uncertain, due to error, noise or unreliable sources. When making decisions based on uncertain data, it is important to quantify and present to the analyst both the aggregated uncertainty of the results and the impact of the sources of that uncertainty. In this paper, we present a new framework to support uncertainty in the visual analytics process, through statistic methods such as uncertainty modeling, propagation and aggregation. We show that data transformations, such as regression, principal component analysis and k-means clustering, can be adapted to account for uncertainty. This framework leads to better visualizations that improve the decision-making process and help analysts gain insight on the analytic process itself. Chan, Y.-H. Correa, C.D. Ma, K.-L. clustering insight uncertainty visual analytics VAST data collections data transformations data visualisation decision making numerous statistical tools statistical analysis uncertainty-aware visual analytics 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) data transformations model fitting principal component analysis uncertainty 2009 vast09--5332610 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Geovisual analytics for self-organizing network data. Cellular radio networks are continually growing in both node count and complexity. It therefore becomes more difficult to manage the networks and necessary to use time and cost effective automatic algorithms to organize the networks neighbor cell relations. There have been a number of attempts to develop such automatic algorithms. Network operators, however, may not trust them because they need to have an understanding of their behavior and of their reliability and performance, which is not easily perceived. This paper presents a novel Web-enabled geovisual analytics approach to exploration and understanding of self-organizing network data related to cells and neighbor cell relations. A demonstrator and case study are presented in this paper, developed in close collaboration with the Swedish telecom company Ericsson and based on large multivariate, time-varying and geospatial data provided by the company. It allows the operators to follow, interact with and analyze the evolution of a self-organizing network and enhance their understanding of how an automatic algorithm configures locally-unique physical cell identities and organizes neighbor cell relations of the network. The geovisual analytics tool is tested with a self-organizing network that is operated by the automatic neighbor relations (ANR) algorithm. The demonstrator has been tested with positive results by a group of domain experts from Ericsson and will be tested in production. Astrom, T. Jern, M. Quan, H.V. case study collaboration geospatial network VAST Ericsson Internet Web-enabled geovisual analytics approach automatic algorithms automatic neighbor relations algorithm cellular radio cellular radio network management data visualisation self-organising feature maps selforganizing network data telecommunication computing telecommunication network management 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) geospatial data sets geovisual analytics multi-dimensional multi-layer self-organizing network time-varying visualization 2009 vast09--5332596 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) A visual analytics system for radio frequency fingerprinting-based localization. Radio frequency (RF) fingerprinting-based techniques for localization are a promising approach for ubiquitous positioning systems, particularly indoors. By finding unique fingerprints of RF signals received at different locations within a predefined area beforehand, whenever a similar fingerprint is subsequently seen again, the localization system will be able to infer a user's current location. However, developers of these systems face the problem of finding reliable RF fingerprints that are unique enough and adequately stable over time. We present a visual analytics system that enables developers of these localization systems to visually gain insight on whether their collected datasets and chosen fingerprint features have the necessary properties to enable a reliable RF fingerprinting-based localization system. The system was evaluated by testing and debugging an existing localization system. Abowd, G.D. Han, Y. Stasko, J. Stuntebeck, E.P. insight visual analytics VAST computational data analysis data analysis data visualisation graphical user interface graphical user interfaces indoor radio interactive visualisation radio direction-finding reliable radio frequency fingerprinting-based localization ubiquitous indoor positioning system visual analytics system 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2009 vast09--5332595 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Finding comparable temporal categorical records: A similarity measure with an interactive visualization. An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher's intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M&M (Match & Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants. Shneiderman, B. Wongsuphasawat, K. categorical time series usability VAST visual analytics 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) M&M Measure Similan similarity search temporal categorical records 2009 vast09--5332586 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates. This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The system's utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 7-15% and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis. Fitzpatrick, P.J. Jankun-Kelly, T.J. Steed, C.A. Swan, J.E. parallel coordinates VAST correlation analysis data visualisation descriptive statistical calculation geophysics computing hurricane trends analysis interactive multivariate representations interactive parallel coordinates interactive visual analysis capabilities multidimensional data explorer regression analysis statistical regression storms visual databases 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) climate study correlation interaction multivariate data regression statistical analysis visual analytics 2009 vast09--5332593 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Proximity-based visualization of movement trace data. The increasing availability of motion sensors and video cameras in living spaces has made possible the analysis of motion patterns and collective behavior in a number of situations. The visualization of this movement data, however, remains a challenge. Although maintaining the actual layout of the data space is often desirable, direct visualization of movement traces becomes cluttered and confusing as the spatial distribution of traces may be disparate and uneven. We present proximity-based visualization as a novel approach to the visualization of movement traces in an abstract space rather than the given spatial layout. This abstract space is obtained by considering proximity data, which is computed as the distance between entities and some number of important locations. These important locations can range from a single fixed point, to a moving point, several points, or even the proximities between the entities themselves. This creates a continuum of proximity spaces, ranging from the fixed absolute reference frame to completely relative reference frames. By combining these abstracted views with the concrete spatial views, we provide a way to mentally map the abstract spaces back to the real space. We demonstrate the effectiveness of this approach, and its applicability to visual analytics problems such as hazard prevention, migration patterns, and behavioral studies. Correa, C.D. Crnovrsanin, T. Ma, K.-L. Muelder, C. visual analytics VAST data visualisation motion sensor proximity-based visualization video camera video cameras 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) linked views movement patterns principal component analysis proximity spatio-temporal visualization temporal trajectories 2009 vast09--5332584 10/12/2009 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) Interactive visual clustering of large collections of trajectories. One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatio-temporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface. Andrienko, G. Andrienko, N. Giannotti, F. Nanni, M. Pedreschi, D. Rinzivillo, S. clustering VAST computationally intensive clustering algorithms data clustering data visualisation interactive systems interactive visual clustering interactive visual interface pattern clustering 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST) classification clustering geovisualization movement data scalable visualization spatio-temporal data trajectories 2009 vast10--5652392 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) DimStiller: Workflows for dimensional analysis and reduction. DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users transform data tables by chaining together different techniques, called operators, into pipelines of expressions. The individual operators have controls and views that are linked together based on the structure of the expression. Users interact with the operator controls to tune parameter choices, with immediate visual feedback guiding the exploration of local neighborhoods of the space of possible data tables. DimStiller also provides global guidance for navigating data-table space through expression templates called workflows, which permit re-use of common patterns of analysis. Bergner, S. Ingram, S. Irvine, V. Möller, T. Munzner, T. Tory, M. VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652398 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual exploration of classification models for risk assessment. In risk assessment applications well informed decisions are made based on huge amounts of multi-dimensional data. In many domains not only the risk of a wrong decision, but in particular the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework tightly integrating interactive visual exploration with machine learning to support the decision making process. The proposed approach uses a series of interactive 2D visualizations of numeric and ordinal data combined with visualization of classification models. These series of visual elements are further linked to the classifier's performance visualized using an interactive performance curve. An interactive decision point on the performance curve allows the decision maker to steer the classification model and instantly identify the critical, cost changing data elements, in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this context the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and accordingly use the most suitable classification model and make more informed and reliable decisions. A case study on data from the Forensic Psychiatry domain reveals the usefulness of the suggested approach. Migut, M. Worring, M. case study machine learning ordinal visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) classification decision boundary visualization interactive visual exploration multi-dimensional space visual analytics 2010 vast10--5652433 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Improving the visual analysis of high-dimensional datasets using quality measures. Modern visualization methods are needed to cope with very high-dimensional data. Efficient visual analytical techniques are required to extract the information content in these data. The large number of possible projections for each method, which usually grow quadrat-ically or even exponentially with the number of dimensions, urges the necessity to employ automatic reduction techniques, automatic sorting or selecting the projections, based on their information-bearing content. Different quality measures have been successfully applied for several specified user tasks and established visualization techniques, like Scatterplots, Scatterplot Matrices or Parallel Coordinates. Many other popular visualization techniques exist, but due to the structural differences, the measures are not directly applicable to them and new approaches are needed. In this paper we propose new quality measures for three popular visualization methods: Radviz, Pixel-Oriented Displays and Table Lenses. Our experiments show that these measures efficiently guide the visual analysis task. Albuquerque, G. Eisemann, M. Lehmann, D.J. Magnor, M. Theisel, H. high-dimensional data parallel coordinates pixel scatterplot VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652443 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction. We present an interactive visual analytics system for classification, iVisClassifier, based on a supervised dimension reduction method, linear discriminant analysis (LDA). Given high-dimensional data and associated cluster labels, LDA gives their reduced dimensional representation, which provides a good overview about the cluster structure. Instead of a single two- or three-dimensional scatter plot, iVisClassifier fully interacts with all the reduced dimensions obtained by LDA through parallel coordinates and a scatter plot. Furthermore, it significantly improves the interactivity and interpretability of LDA. LDA enables users to understand each of the reduced dimensions and how they influence the data by reconstructing the basis vector into the original data domain. By using heat maps, iVisClassifier gives an overview about the cluster relationship in terms of pairwise distances between cluster centroids both in the original space and in the reduced dimensional space. Equipped with these functionalities, iVisClassifier supports users' classification tasks in an efficient way. Using several facial image data, we show how the above analysis is performed. Choo, J. Kihm, J. Lee, H. Park, H. cluster dimension reduction high-dimensional data overview parallel coordinates visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652450 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators. Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. However, many clustering algorithms require the user to set a large number of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clusters present or the significance of the clusters. In this paper, we propose a method for ranking subspaces for clustering which overcomes many of the above limitations. First we carry out a transformation from parametric space to discrete image space where the data are represented by a grid-based density field. Then we apply so-called connected morphological operators on this density field of astronomical objects that provides visual support for the analysis of the important subspaces. Clusters in subspaces correspond to high-intensity regions in the density image. The importance of a cluster is measured by a new quality criterion based on the dynamics of local maxima of the density. Connected operators are able to extract such regions with an indication of the number of clusters present. The subspaces are visualized during computation of the quality measure, so that the user can interact with the system to improve the results. In the result stage, we use three visualization toolkits linked within a graphical user interface so that the user can perform an in-depth exploration of the ranked subspaces. Evaluation based on synthetic as well as real astronomical datasets demonstrates the power of the new method. We recover various known astronomical relations directly from the data with little or no a pri- - ori assumptions. Hence, our method holds good prospects for discovering new relations as well. Buddelmeijer, H. Ferdosi, B.J. Roerdink, J. Trager, S. Wilkinson, M. cluster clustering evaluation high-dimensional data VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) astronomical data clustering high-dimensional data connected morphological operators subspace finding visual exploration 2010 vast10--5652460 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Flow-based scatterplots for sensitivity analysis. Visualization of multi-dimensional data is challenging due to the number of complex correlations that may be present in the data but that are difficult to be visually identified. One of the main causes for this problem is the inherent loss of information that occurs when high-dimensional data is projected into 2D or 3D. Although 2D scatterplots are ubiquitous due to their simplicity and familiarity, there are not a lot of variations on their basic metaphor. In this paper, we present a new way of visualizing multidimensional data using scatterplots. We extend 2D scatterplots using sensitivity coefficients to highlight local variation of one variable with respect to another. When applied to a scatterplot, these sensitivities can be understood as velocities, and the resulting visualization resembles a flow field. We also present a number of operations, based on flow-field analysis, that help users navigate, select and cluster points in an efficient manner. We show the flexibility and generality of this approach using a number of multidimensional data sets across different domains. Chan, Y.-H. Correa, C.D. Ma, K.-L. cluster high-dimensional data scatterplot VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) data transformations model fitting principal component analysis uncertainty 2010 vast10--5652467 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Anomaly detection in GPS data based on visual analytics. Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using the approach of visual analytics: a conditional random field (CRF) model is used as the machine learning component for anomaly detection in streaming GPS traces. A visualization component and an interactive user interface are built to visualize the data stream, display significant analysis results (i.e., anomalies or uncertain predications) and hidden information extracted by the anomaly detection model, which enable human experts to observe the real-time data behavior and gain insights into the data flow. Human experts further provide guidance to the machine learning model through the interaction tools; the learning model is then incrementally improved through an active learning procedure. Chen, B. Liao, Z. Yu, Y. interaction machine learning visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652478 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Discovering bits of place histories from people's activity traces. Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Significant and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile phone operators or photos on photo sharing web sites. We developed a suite of visual analytics methods for reconstructing past events from these activity traces. Our tools combine geocomputations, interactive geovisualizations and statistical methods to enable integrated analysis of the spatial, temporal, and thematic components of the data, including numeric attributes and texts. We demonstrate the utility of our approach on two large real data sets, mobile phone calls in Milano during 9 days and flickr photos made on British Isles during 5 years. Andrienko, G. Andrienko, N. Mladenov, M. Mock, M. Politz, C. visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) event detection geovisualization scalable visualization spatio-temporal data time series analysis 2010 vast10--5652484 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) A visual analytics approach to model learning. The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more efficient, problem-driven, and engaging. Utilizing a sequence segmentation task with a Hidden Markov Model as an example, we assign each token in the sequence a feature vector based on its various properties within the sequence. These vectors are then clustered according to similarity, generating a layout of the individual tokens in form of a node link diagram where the length of the links is determined by the feature vector similarity. Users may then tune the weights of the feature vector components to improve the segmentation, which is visualized as a better separation of the clusters. Also, as individual clusters represent different classes, the user can now work at the cluster level to define token classes, instead of labelling one entry at time. Inconsistent entries visually identify themselves by locating at the periphery of clusters, and the user then helps refine the model by resolving these inconsistencies. Our system therefore makes efficient use of the knowledge of its users, only requesting user assistance for non-trivial data items. It so allows users to visually analyse data at a higher, more abstract level, improving scalability. Garg, S. Mueller, K. Ramakrishnan, I.V. cluster visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) data clustering human-computer interaction visual knowledge discovery visual knowledge representation 2010 vast10--5652520 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Multidimensional data dissection using attribute relationship graphs. Visual exploration and analysis is a process of discovering and dissecting the abundant and complex attribute relationships that pervade multidimensional data. Recent research has identified and characterized patterns of multiple coordinated views, such as cross-filtered views, in which rapid sequences of simple interactions can be used to express queries on subsets of attribute values. In visualizations designed around these patterns, for the most part, distinct views serve to visually isolate each attribute from the others. Although the brush-and-click simplicity of visual isolation facilitates discovery of many-to-many relationships between attributes, dissecting these relationships into more fine-grained one-to-many relationships is interactively tedious and, worse, visually fragmented over prolonged sequences of queries. This paper describes: (1) a method for interactively dissecting multidimensional data by iteratively slicing and manipulating a multigraph representation of data values and value co-occurrences; and (2) design strategies for extending the construction of coordinated multiple view interfaces for dissection as well as discovery of attribute relationships in multidimensional data sets. Using examples from different domains, we describe how attribute relationship graphs can be combined with cross-filtered views, modularized for reuse across designs, and integrated into broader visual analysis tools. The exploratory and analytic utility of these examples suggests that an attribute relationship graph would be a useful addition to a wide variety of visual analysis tools. Weaver, C. coordinated views graph VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652530 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual market sector analysis for financial time series data. The massive amount of financial time series data that originates from the stock market generates large amounts of complex data of high interest. However, adequate solutions that can effectively handle the information in order to gain insight and to understand the market mechanisms are rare. In this paper, we present two techniques and applications that enable the user to interactively analyze large amounts of time series data in real-time in order to get insight into the development of assets, market sectors, countries, and the financial market as a whole. The first technique allows users to quickly analyze combinations of single assets, market sectors as well as countries, compare them to each other, and to visually discover the periods of time where market sectors and countries get into turbulence. The second application clusters a selection of large amounts of financial time series data according to their similarity, and analyzes the distribution of the assets among market sectors. This allows users to identify the characteristic graphs which are representative for the development of a particular market sector, and also to identify the assets which behave considerably differently compared to other assets in the same sector. Both applications allow the user to perform investigative exploration techniques and interactive visual analysis in real-time. Gruse, T. Jenny, M. Keim, D.A. Ziegler, H. financial insight time series VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) explorative analysis financial information visualization time series clustering time series data visual analytics 2010 vast10--5652940 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Two-stage framework for a topology-based projection and visualization of classified document collections. During the last decades, electronic textual information has become the world's largest and most important information source. Daily newspapers, books, scientific and governmental publications, blogs and private messages have grown into a wellspring of endless information and knowledge. Since neither existing nor new information can be read in its entirety, we rely increasingly on computers to extract and visualize meaningful or interesting topics and documents from this huge information reservoir. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topological analysis of high dimensional document point clouds given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To demonstrate the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections. Ertl, T. Heyer, G. Koch, S. Oesterling, P. Scheuermann, G. Teresniak, S. Weber, G. document usability VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652931 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Understanding text corpora with multiple facets. Text visualization becomes an increasingly more important research topic as the need to understand massive-scale textual information is proven to be imperative for many people and businesses. However, it is still very challenging to design effective visual metaphors to represent large corpora of text due to the unstructured and high-dimensional nature of text. In this paper, we propose a data model that can be used to represent most of the text corpora. Such a data model contains four basic types of facets: time, category, content (unstructured), and structured facet. To understand the corpus with such a data model, we develop a hybrid visualization by combining the trend graph with tag-clouds. We encode the four types of data facets with four separate visual dimensions. To help people discover evolutionary and correlation patterns, we also develop several visual interaction methods that allow people to interactively analyze text by one or more facets. Finally, we present two case studies to demonstrate the effectiveness of our solution in support of multi-faceted visual analysis of text corpora. Lian, X. Liu, S. Shi, L. Tan, L. Wei, F. Zhou, M.X. graph interaction text VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) multi-facet data visualization text visualization 2010 vast10--5652932 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) VizCept: Supporting synchronous collaboration for constructing visualizations in intelligence analysis. In this paper, we present a new web-based visual analytics system, VizCept, which is designed to support fluid, collaborative analysis of large textual intelligence datasets. The main approach of the design is to combine individual workspace and shared visualization in an integrated environment. Collaborating analysts will be able to identify concepts and relationships from the dataset based on keyword searches in their own workspace and collaborate visually with other analysts using visualization tools such as a concept map view and a timeline view. The system allows analysts to parallelize the work by dividing initial sets of concepts, investigating them on their own workspace, and then integrating individual findings automatically on shared visualizations with support for interaction and personal graph layout in real time, in order to develop a unified plot. We highlight several design considerations that promote communication and analytic performance in small team synchronous collaboration. We report the result of a pair of case study applications including collaboration and communication methods, analysis strategies, and user behaviors under a competition setting in the same location at the same time. The results of these demonstrate the tool's effectiveness for synchronous collaborative construction and use of visualizations in intelligence data analysis. Andrews, C. Chung, H. Kanna, R. Massjouni, N. North, C. Yang, S. case study collaboration graph graph layout intelligence analysis interaction visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) collaborative visualization intelligence analysis text and document data 2010 vast10--5652922 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Diamonds in the rough: Social media visual analytics for journalistic inquiry. Journalists increasingly turn to social media sources such as Facebook or Twitter to support their coverage of various news events. For large-scale events such as televised debates and speeches, the amount of content on social media can easily become overwhelming, yet still contain information that may aid and augment reporting via individual content items as well as via aggregate information from the crowd's response. In this work we present a visual analytic tool, Vox Civitas, designed to help journalists and media professionals extract news value from large-scale aggregations of social media content around broadcast events. We discuss the design of the tool, present the text analysis techniques used to enable the presentation, and provide details on the visual and interaction design. We provide an exploratory evaluation based on a user study in which journalists interacted with the system to explore and report on a dataset of over one hundred thousand twitter messages collected during the U.S. State of the Union presidential address in 2010. Diakopoulos, N. Kivran-Swaine, F. Naaman, M. evaluation interaction social text user study visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) computational journalism computer assisted reporting sensemaking social media 2010 vast10--5652926 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Visual readability analysis: How to make your writings easier to read. We present a tool that is specifically designed to support a writer in revising a draft-version of a document. In addition to showing which paragraphs and sentences are difficult to read and understand, we assist the reader in understanding why this is the case. This requires features that are expressive predictors of readability, and are also semantically understandable. In the first part of the paper, we therefore discuss a semi-automatic feature selection approach that is used to choose appropriate measures from a collection of 141 candidate readability features. In the second part, we present the visual analysis tool VisRA, which allows the user to analyze the feature values across the text and within single sentences. The user can choose different visual representations accounting for differences in the size of the documents and the availability of information about the physical and logical layout of the documents. We put special emphasis on providing as much transparency as possible to ensure that the user can purposefully improve the readability of a sentence. Several case-studies are presented that show the wide range of applicability of our tool. Keim, D.A. Oelke, D. Spretke, D. Stoffel, A. document text VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652910 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) NetClinic: Interactive visualization to enhance automated fault diagnosis in enterprise networks. Diagnosing faults in an operational computer network is a frustrating, time-consuming exercise. Despite advances, automatic diagnostic tools are far from perfect: they occasionally miss the true culprit and are mostly only good at narrowing down the search to a few potential culprits. This uncertainty and the inability to extract useful sense from tool output renders most tools not usable to administrators. To bridge this gap, we present NetClinic, a visual analytics system that couples interactive visualization with an automated diagnostic tool for enterprise networks. It enables administrators to verify the output of the automatic analysis at different levels of detail and to move seamlessly across levels while retaining appropriate context. A qualitative user study shows that NetClinic users can accurately identify the culprit, even when it is not present in the suggestions made by the automated component. We also find that supporting a variety of sensemaking strategies is a key to the success of systems that enhance automated diagnosis. Kandula, S. Lee, B. Liu, Z. Mahajan, R. network sensemaking uncertainty user study visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) information visualization network diagnosis semantic graph layout sensemaking visual analytics 2010 vast10--5652895 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Geo-historical context support for information foraging and sensemaking: Conceptual model, implementation, and assessment. Information foraging and sensemaking with heterogeneous information are context-dependent activities. Thus visual analytics tools to support these activities must incorporate context. But, context is a difficult concept to define, model, and represent. Creating and representing context in support of visually-enabled reasoning about complex problems with complex information is a complementary but different challenge than that addressed in context-aware computing. In the latter, the goal is automated adaptation of the system to meet user needs for applications such as mobile location-based services where information about the location, the user, and the user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sensemaking, the user is likely to take an active role in foraging for the contextual information needed to support sensemaking in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytical reasoning in crisis management and humanitarian relief. The challenges stem from the diverse forms of information that can provide context and difficulty in defining and operationalizing context itself. Here, we pay particular attention to document foraging to support construction of the geographic and historical context within which monitoring and sensemaking can be carried out. Specifically, we present the concept of geo-historical context (GHC) and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application, a web-based tool that supports document foraging and sensemaking. MacEachren, A.M. Tomaszewski, B. document geographic sensemaking visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) context foraging geographic information retrieval mapping sensemaking text analysis 2010 vast10--5652896 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Real-time aggregation of Wikipedia data for visual analytics. Wikipedia has been built to gather encyclopedic knowledge using a collaborative social process that has proved its effectiveness. However, the workload required for raising the quality and increasing the coverage of Wikipedia is exhausting the community. Based on several participatory design sessions with active Wikipedia contributors (a.k.a. Wikipedians), we have collected a set of measures related to Wikipedia activity that, if available and visualized effectively, could spare a lot of monitoring time to these Wikipedians, allowing them to focus on quality and coverage of Wikipedia instead of spending their time navigating heavily to track vandals and copyright infringements. However, most of these measures cannot be computed on the fly using the available Wikipedia API. Therefore, we have designed an open architecture called WikiReactive to compute incrementally and maintain several aggregated measures on the French Wikipedia. This aggregated data is available as a Web Service and can be used to overlay information on Wikipedia articles through Wikipedia Skins or for new services for Wikipedians or people studying Wikipedia. This article describes the architecture, its performance and some of its uses. Boukhelifa, N. Chevalier, F. Fekete, J.-D. social visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652885 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Click2Annotate: Automated Insight Externalization with rich semantics. Insight Externalization (IE) refers to the process of capturing and recording the semantics of insights in decision making and problem solving. To reduce human effort, Automated Insight Externalization (AIE) is desired. Most existing IE approaches achieve automation by capturing events (e.g., clicks and key presses) or actions (e.g., panning and zooming). In this paper, we propose a novel AIE approach named Click2Annotate. It allows semi-automatic insight annotation that captures low-level analytics task results (e.g., clusters and outliers), which have higher semantic richness and abstraction levels than actions and events. Click2Annotate has two significant benefits. First, it reduces human effort required in IE and generates annotations easy to understand. Second, the rich semantic information encoded in the annotations enables various insight management activities, such as insight browsing and insight retrieval. We present a formal user study that proved this first benefit. We also illustrate the second benefit by presenting the novel insight management activities we developed based on Click2Annotate, namely scented insight browsing and faceted insight search. Barlowe, S. Chen, Y. Yang, J. insight user study zooming VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) annotation decision making insight management multidimensional visualization visual analytics 2010 vast10--5652890 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Interactive querying of temporal data using a comic strip metaphor. Finding patterns in temporal data is an important data analysis task in many domains. Static visualizations can help users easily see certain instances of patterns, but are not specially designed to support systematic analysis tasks, such as finding all instances of a pattern automatically. VizPattern is an interactive visual query environment that uses a comic strip metaphor to enable users to easily and quickly define and locate complex temporal patterns. Evaluations provide evidence that VizPattern is applicable in many domains, and that it enables a wide variety of users to answer questions about temporal data faster and with fewer errors than existing state-of-the-art visual analysis systems. Jin, J. Szekely, P. VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5652879 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) A closer look at note taking in the co-located collaborative visual analytics process. This paper highlights the important role that record-keeping (i.e. taking notes and saving charts) plays in collaborative data analysis within the business domain. The discussion of record-keeping is based on observations from a user study in which co-located teams worked on collaborative visual analytics tasks using large interactive wall and tabletop displays. Part of our findings is a collaborative data analysis framework that encompasses note taking as one of the main activities. We observed that record-keeping was a critical activity within the analysis process. Based on our observations, we characterize notes according to their content, scope, and usage, and describe how they fit into a process of collaborative data analysis. We then discuss suggestions for the design of collaborative visual analytics tools. Mahyar, N. Sarvghad, A. Tory, M. business user study visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) collaboration history note taking provenance recording tabletop wall display 2010 vast10--5652880 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) An exploratory study of co-located collaborative visual analytics around a tabletop display. Co-located collaboration can be extremely valuable during complex visual analytics tasks. This paper presents an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cam-biera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration influenced how well they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive novel design implications for future co-located collaborative tabletop problem solving systems. Czerwinski, M. Fisher, D. Inkpen, K. Isenberg, P. Morris, M.R. collaboration visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 vast10--5653598 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Helping users recall their reasoning process. The final product of an analyst's investigation using a visualization is often a report of the discovered knowledge, as well as the methods employed and reasoning behind the discovery. We believe that analysts may have difficulty keeping track of their knowledge discovery process and will require tools to assist in accurately recovering their reasoning. We first report on a study examining analysts' recall of their strategies and methods, demonstrating their lack of memory of the path of knowledge discovery. We then explore whether a tool visualizing the steps of the visual analysis can aid users in recalling their reasoning process. The results of our second study indicate that visualizations of interaction logs can serve as an effective memory aid, allowing analysts to recall additional details of their strategies and decisions. Chang, R. Dou, W. Hawkins, M. Lipford, H.R. Stukes, F. interaction VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) reasoning process visual analytics visualization 2010 vast10--5653599 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Comparing different levels of interaction constraints for deriving visual problem isomorphs. Interaction and manual manipulation have been shown in the cognitive science literature to play a critical role in problem solving. Given different types of interactions or constraints on interactions, a problem can appear to have different degrees of difficulty. While this relationship between interaction and problem solving has been well studied in the cognitive science literatures, the visual analytics community has yet to exploit this understanding for analytical problem solving. In this paper, we hypothesize that constraints on interactions and constraints encoded in visual representations can lead to strategies of varying effectiveness during problem solving. To test our hypothesis, we conducted a user study in which participants were given different levels of interaction constraints when solving a simple math game called Number Scrabble. Number Scrabble is known to have an optimal visual problem isomorph, and the goal of this study is to learn if and how the participants could derive the isomorph and to analyze the strategies that the participants utilize in solving the problem. Our results indicate that constraints on interactions do affect problem solving, and that while the optimal visual isomorph is difficult to derive, certain interaction constraints can lead to a higher chance of deriving the isomorph. Chang, R. Dou, W. Harrison, L. Jeong, D.H. Ribarsky, W. Ryan, R. Wang, X. Ziemkiewicz, C. interaction user study visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) interaction problem solving visual isomorph 2010 vast10--5653587 10/25/2010 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) Towards the Personal Equation of Interaction: The impact of personality factors on visual analytics interface interaction. These current studies explored the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered 3 psychometric measures designed to assess Locus of Control, Extraversion, and Neuroticism. Participants were then asked to complete multiple procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, results analyses demonstrated personality factors also predicted the number of insights participants reported while completing the tasks in each interface. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction. Fisher, B. Green, T.M. interaction visual analytics VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) cognition and perception theory embodied cognition visual analytics visualization taxonomies and models 2010 vast11--6102443 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual social network analytics for relationship discovery in the enterprise. As people continue to author and share increasing amounts of information in social media, the opportunity to leverage such information for relationship discovery tasks increases. In this paper, we describe a set of systems that mine, aggregate, and infer a social graph from social media inside an enterprise, resulting in over 73 million relationships between 450,000 people. We then describe SaNDVis, a novel visual analytics tool that supports people-centric tasks like expertise location, team building, and team coordination in the enterprise. We also provide details of a 12-month-long, large-scale deployment to almost 1,800 users from which we extract dominant use cases from log and interview data. By integrating social position, evidence, and facets into SaNDVis, we demonstrate how users can use a visual analytics tool to reflect on existing relationships as well as build new relationships in an enterprise setting. Guy, I. Jacovi, M. Perer, A. Ronen, I. Uziel, E. graph network social visual analytics VAST blogs databases information services internet social network services tagging visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) information discovery social data mining social networks social visualization 2011 vast11--6102457 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analytics decision support environment for epidemic modeling and response evaluation. In modeling infectious diseases, scientists are studying the mechanisms by which diseases spread, predicting the future course of the outbreak, and evaluating strategies applied to control an epidemic. While recent work has focused on accurately modeling disease spread, less work has been performed in developing interactive decision support tools for analyzing the future course of the outbreak and evaluating potential disease mitigation strategies. The absence of such tools makes it difficult for researchers, analysts and public health officials to evaluate response measures within outbreak scenarios. As such, our research focuses on the development of an interactive decision support environment in which users can explore epidemic models and their impact. This environment provides a spatiotemporal view where users can interactively utilize mitigative response measures and observe the impact of their decision over time. Our system also provides users with a linked decision history visualization and navigation tool that support the simultaneous comparison of mortality and infection rates corresponding to different response measures at different points in time. Afzal, S. Ebert, D.S. Maciejewski, R. evaluation history navigation visual analytics VAST adaptation models analytical models diseases history spatiotemporal phenomena visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102435 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analytic roadblocks for novice investigators. We have observed increasing interest in visual analytics tools and their applications in investigative analysis. Despite the growing interest and substantial studies regarding the topic, understanding the major roadblocks of using such tools from novice users' perspectives is still limited. Therefore, we attempted to identify such "visual analytic roadblocks" for novice users in an investigative analysis scenario. To achieve this goal, we reviewed the existing models, theories, and frameworks that could explain the cognitive processes of human-visualization interaction in investigative analysis. Then, we conducted a qualitative experiment with six novice participants, using a slightly modified version of pair analytics, and analyzed the results through the open-coding method. As a result, we came up with four visual analytic roadblocks and explained these roadblocks using existing cognitive models and theories. We also provided design suggestions to overcome these roadblocks. Fisher, B. Kwon, B.C. Yi, J.S. experiment interaction visual analytics VAST analytical models calendars cognition context humans visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) cognitive model framework investigative analysis qualitative experiment roadblock visual analytics 2011 vast11--6102455 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analysis of route diversity. Route suggestion is an important feature of GPS navigation systems. Recently, Microsoft T-drive has been enabled to suggest routes chosen by experienced taxi drivers for given source/destination pairs in given time periods, which often take less time than the routes calculated according to distance. However, in real environments, taxi drivers may use different routes to reach the same destination, which we call route diversity. In this paper we first propose a trajectory visualization method that examines the regions where the diversity exists and then develop several novel visualization techniques to display the high dimensional attributes and statistics associated with different routes to help users analyze diversity patterns. Our techniques have been applied to the real trajectory data of thousands of taxis and some interesting findings about route diversity have been obtained. We further demonstrate that our system can be used not only to suggest better routes for drivers but also to analyze traffic bottlenecks for transportation management. Gao, Y. Liu, H. Liu, S. Lu, L. Ni, L.M. Qu, H. navigation statistics VAST cities and towns data visualization layout roads trajectory vehicles visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102447 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Supporting effective common ground construction in Asynchronous Collaborative Visual Analytics. Asynchronous Collaborative Visual Analytics (ACVA) leverages group sensemaking by releasing the constraints on when, where, and who works collaboratively. A significant task to be addressed before ACVA can reach its full potential is effective common ground construction, namely the process in which users evaluate insights from individual work to develop a shared understanding of insights and collectively pool them. This is challenging due to the lack of instant communication and scale of collaboration in ACVA. We propose a novel visual analytics approach that automatically gathers, organizes, and summarizes insights to form common ground with reduced human effort. The rich set of visualization and interaction techniques provided in our approach allows users to effectively and flexibly control the common ground construction and review, explore, and compare insights in detail. A working proto-type of the approach has been implemented. We have conducted a case study and a user study to demonstrate its effectiveness. Alsakran, J. Barlowe, S. Chen, Y. Yang, J. Zhao, Y. case study collaboration interaction sensemaking user study visual analytics VAST animation collaboration correlation history semantics visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) asynchronous collaboration insight multidimensional visualization visual analytics 2011 vast11--6102456 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) SensePlace2: GeoTwitter analytics support for situational awareness. Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media. Blanford, J. Jaiswal, A. MacEachren, A.M. Mitra, P. Pezanowski, S. Robinson, A.C. Savelyev, A. Zhang, X. awareness geographic overview social VAST crisis management decision making media organizations twitter visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) crisis management geovisualization scenario-based design situational awareness social media analytics spatio-temporal analysis text analytics 2011 vast11--6102458 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) SAVE: Sensor anomaly visualization engine. Diagnosing a large-scale sensor network is a crucial but challenging task. Particular challenges include the resource and bandwidth constraints on sensor nodes, the spatiotemporally dynamic network behaviors, and the lack of accurate models to understand such behaviors in a hostile environment. In this paper, we present the Sensor Anomaly Visualization Engine (SAVE), a system that fully leverages the power of both visualization and anomaly detection analytics to guide the user to quickly and accurately diagnose sensor network failures and faults. SAVE combines customized visualizations over separate sensor data facets as multiple coordinated views. Temporal expansion model, correlation graph and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a case study with real-world sensor network system and administrators, we demonstrate that SAVE is able to help better locate the system problem and further identify the root cause of major sensor network failure scenarios. He, Y. Li, R. Liao, Q. Shi, L. Striegel, A. Su, Z. case study coordinated views graph network VAST correlation data visualization network topology routing time series analysis topology wireless sensor networks 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102450 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pointwise local pattern exploration for sensitivity analysis. Sensitivity analysis is a powerful method for discovering the significant factors that contribute to targets and understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. Incorporating sensitivity analysis in visual analytic tools is essential for multivariate phenomena analysis. However, most current multivariate visualization techniques do not allow users to explore local patterns individually for understanding the sensitivity from a pointwise view. In this paper, we present a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. During exploration, users are able to interactively change the derivative coefficients to perform sensitivity analysis based on different requirements as well as their domain knowledge. Each local pattern is assigned an outlier factor, so that users can quickly identify anomalous local patterns that do not conform with the global pattern. Users can also compare the local pattern with the global pattern both visually and statistically. Finally, the local pattern is integrated into the original attribute space using color mapping and jittering, which reveals the distribution of the partial derivatives. Case studies with real datasets are used to investigate the effectiveness of the visualizations and interactions. Guo, Z. Ruiz, C. Rundensteiner, E.A. Ward, M.O. color interaction VAST analytical models data mining image color analysis sensitivity analysis vectors visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) knowledge discovery local pattern visualization sensitivity analysis 2011 vast11--6102437 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Perception-based visual quality measures. In recent years diverse quality measures to support the exploration of high-dimensional data sets have been proposed. Such measures can be very useful to rank and select information-bearing projections of very high dimensional data, when the visual exploration of all possible projections becomes unfeasible. But even though a ranking of the low dimensional projections may support the user in the visual exploration task, different measures deliver different distances between the views that do not necessarily match the expectations of human perception. As an alternative solution, we propose a perception-based approach that, similar to the existing measures, can be used to select information bearing projections of the data. Specifically, we construct a perceptual embedding for the different projections based on the data from a psychophysics study and multi-dimensional scaling. This embedding together with a ranking function is then used to estimate the value of the projections for a specific user task in a perceptual sense. Albuquerque, G. Eisemann, M. Magnor, M. high-dimensional data perception VAST atmospheric measurements correlation data visualization humans particle measurements training visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102461 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) ParallelTopics: A probabilistic approach to exploring document collections. Scalable and effective analysis of large text corpora remains a challenging problem as our ability to collect textual data continues to increase at an exponential rate. To help users make sense of large text corpora, we present a novel visual analytics system, Parallel-Topics, which integrates a state-of-the-art probabilistic topic model Latent Dirichlet Allocation (LDA) with interactive visualization. To describe a corpus of documents, ParallelTopics first extracts a set of semantically meaningful topics using LDA. Unlike most traditional clustering techniques in which a document is assigned to a specific cluster, the LDA model accounts for different topical aspects of each individual document. This permits effective full text analysis of larger documents that may contain multiple topics. To highlight this property of the model, ParallelTopics utilizes the parallel coordinate metaphor to present the probabilistic distribution of a document across topics. Such representation allows the users to discover single-topic vs. multi-topic documents and the relative importance of each topic to a document of interest. In addition, since most text corpora are inherently temporal, ParallelTopics also depicts the topic evolution over time. We have applied ParallelTopics to exploring and analyzing several text corpora, including the scientific proposals awarded by the National Science Foundation and the publications in the VAST community over the years. To demonstrate the efficacy of ParallelTopics, we conducted several expert evaluations, the results of which are reported in this paper. Chang, R. Dou, W. Ribarsky, W. Wang, X. cluster clustering document text visual analytics VAST analytical models entropy probabilistic logic semantics text analysis text processing visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102441 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Orion: A system for modeling, transformation and visualization of multidimensional heterogeneous networks. The study of complex activities such as scientific production and software development often require modeling connections among heterogeneous entities including people, institutions and artifacts. Despite numerous advances in algorithms and visualization techniques for understanding such social networks, the process of constructing network models and performing exploratory analysis remains difficult and time-consuming. In this paper we present Orion, a system for interactive modeling, transformation and visualization of network data. Orion's interface enables the rapid manipulation of large graphs &#8211; including the specification of complex linking relationships &#8211; using simple drag-and-drop operations with desired node types. Orion maps these user interactions to statements in a declarative workflow language that incorporates both relational operators (e.g., selection, aggregation and joins) and network analytics (e.g., centrality measures). We demonstrate how these features enable analysts to flexibly construct and compare networks in domains such as online health communities, academic collaboration and distributed software development. Heer, J. Perer, A. collaboration network social VAST analytical models communities data models data visualization joining processes social network services 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) data management data transformation end-user programming graphs social network analysis visualization 2011 vast11--6102446 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Obvious: A meta-toolkit to encapsulate information visualization toolkits &#8211; One toolkit to bind them all. This article describes "Obvious": a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and Rapid-Miner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics. Baudel, T. Fekete, J.-D. Hemery, P. Wood, J. toolkit visual analytics VAST data models data visualization java libraries observers visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102449 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Observation-level interaction with statistical models for visual analytics. In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus "observation") in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools. Endert, A. Han, C. House, L. Leman, S. Maiti, D. North, C. insight interaction sensemaking visual analytics VAST analytical models data models data visualization layout principal component analysis visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) observation-level interaction statistical models visual analytics 2011 vast11--6102440 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Network-based visual analysis of tabular data. Tabular data are pervasive. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience. Liu, Z. Navathe, S.B. Stasko, J. graph network VAST aggregates cities and towns data visualization organizations relational databases semantics visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102439 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Interactive visual comparison of multiple trees. Traditionally, the visual analysis of hierarchies, respectively, trees, is conducted by focusing on one given hierarchy. However, in many research areas multiple, differing hierarchies need to be analyzed simultaneously in a comparative way - in particular to highlight differences between them, which sometimes can be subtle. A prominent example is the analysis of so-called phylogenetic trees in biology, reflecting hierarchical evolutionary relationships among a set of organisms. Typically, the analysis considers multiple phylogenetic trees, either to account for statistical significance or for differences in derivation of such evolutionary hierarchies; for example, based on different input data, such as the 16S ribosomal RNA and protein sequences of highly conserved enzymes. The simultaneous analysis of a collection of such trees leads to more insight into the evolutionary process. We introduce a novel visual analytics approach for the comparison of multiple hierarchies focusing on both global and local structures. A new tree comparison score has been elaborated for the identification of interesting patterns. We developed a set of linked hierarchy views showing the results of automatic tree comparison on various levels of details. This combined approach offers detailed assessment of local and global tree similarities. The approach was developed in close cooperation with experts from the evolutionary biology domain. We apply it to a phylogenetic data set on bacterial ancestry, demonstrating its application benefit. Bremm, S. Hamacherk, K. Hess, M. Schreck, T. Weil, P. von Landesberger, T. hierarchies hierarchy insight visual analytics VAST data visualization image color analysis organisms phylogeny vegetation visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102451 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Interactive decision making using dissimilarity to visually represented prototypes. To make informed decisions, an expert has to reason with multi-dimensional, heterogeneous data and analysis results of these. Items in such datasets are typically represented by features. However, as argued in cognitive science, features do not yield an optimal space for human reasoning. In fact, humans tend to organize complex information in terms of prototypes or known cases rather than in absolute terms. When confronted with unknown data items, humans assess them in terms of similarity to these prototypical elements. Interestingly, an analogues similarity-to-prototype approach, where prototypes are taken from the data, has been successfully applied in machine learning. Combining such a machine learning approach with human prototypical reasoning in a Visual Analytics context requires to integrate similarity-based classification with interactive visualizations. To that end, the data prototypes should be visually represented to trigger direct associations to cases familiar to the domain experts. In this paper, we propose a set of highly interactive visualizations to explore data and classification results in terms of dissimilarities to visually represented prototypes. We argue that this approach not only supports human reasoning processes, but is also suitable to enhance understanding of heterogeneous data. The proposed framework is applied to a risk assessment case study in Forensic Psychiatry. Migut, M. Worring, M. van Gemert, J.C. case study machine learning visual analytics VAST data mining data visualization humans image color analysis prototypes visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) dissimilarity based classification dissimilarity based visualization interactive visualization prototypes visual analytics 2011 vast11--6102445 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) How locus of control influences compatibility with visualization style. Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. In this paper, we extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as "locus of control," which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling, and specifically focus on the overall layout style of the visualizations. We conduct a user study with four visualizations that gradually shift from an indentation metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. We discuss a possible explanation for this relationship based in cognitive psychology and propose that these results can be used to better understand how people use visualizations and how to adapt visual analytics design to an individual user's needs. Chang, R. Crouser, R.J. Ribarsky, W. Su, S.L. Yauilla, A.R. Ziemkiewicz, C. color interaction user study visual analytics VAST correlation data visualization encoding layout particle measurements visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102448 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Guiding feature subset selection with an interactive visualization. We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features. Bannach, A. Davey, J. Kohlhammer, J. May, T. Ruppert, T. filter high-dimensional data VAST algorithm design and analysis atmospheric measurements correlation data visualization particle measurements sorting visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102442 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) G-PARE: A visual analytic tool for comparative analysis of uncertain graphs. There are a growing number of machine learning algorithms which operate on graphs. Example applications for these algorithms include predicting which customers will recommend products to their friends in a viral marketing campaign using a customer network, predicting the topics of publications in a citation network, or predicting the political affiliations of people in a social network. It is important for an analyst to have tools to help compare the output of these machine learning algorithms. In this work, we present G-PARE, a visual analytic tool for comparing two uncertain graphs, where each uncertain graph is produced by a machine learning algorithm which outputs probabilities over node labels. G-PARE provides several different views which allow users to obtain a global overview of the algorithms output, as well as focused views that show subsets of nodes of interest. By providing an adaptive exploration environment, G-PARE guides the users to places in the graph where two algorithms predictions agree and places where they disagree. This enables the user to follow cascades of misclassifications by comparing the algorithms outcome with the ground truth. After describing the features of G-PARE, we illustrate its utility through several use cases based on networks from different domains. Getoor, L. Namata, G. Sharara, H. Singh, L. Sopan, A. graph machine learning network overview social VAST data models data visualization machine learning algorithms prediction algorithms predictive models uncertainty visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) comparative analysis model comparison uncertain graphs visualizing uncertainty 2011 vast11--6102454 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) From movement tracks through events to places: Extracting and characterizing significant places from mobility data. We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales. Andrienko, G. Andrienko, N. Hurter, C. Rinzivillo, S. Wrobel, S. clustering visual analytics VAST aggregates clustering algorithms context data mining time series analysis trajectory visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) movement spatial clustering spatial events spatio-temporal clustering spatio-temporal data trajectories 2011 vast11--6102438 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Characterizing the intelligence analysis process: Informing visual analytics design through a longitudinal field study. While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research community's understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a ten-week period. Based upon study findings, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis. Kang, Y. Stasko, J. field study intelligence analysis visual analytics VAST analytical models artificial intelligence collaboration electronic publishing information services internet visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) intelligence analysis qualitatvie user study 2011 vast11--6102453 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) BaobabView: Interactive construction and analysis of decision trees. We present a system for the interactive construction and analysis of decision trees that enables domain experts to bring in domain specific knowledge. We identify different user tasks and corresponding requirements, and develop a system incorporating a tight integration of visualization, interaction and algorithmic support. Domain experts are supported in growing, pruning, optimizing and analysing decision trees. Furthermore, we present a scalable decision tree visualization optimized for exploration. We show the effectiveness of our approach by applying the methods to two use cases. The first case illustrates the advantages of interactive construction, the second case demonstrates the effectiveness of analysis of decision trees and exploration of the structure of the data. van Wijk, J.J. van den Elzen, S. interaction VAST accuracy algorithm design and analysis data visualization decision trees histograms impurities training 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 2011 vast11--6102462 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Analysis of large digital collections with interactive visualization. To make decisions about the long-term preservation and access of large digital collections, archivists gather information such as the collections' contents, their organizational structure, and their file format composition. To date, the process of analyzing a collection — from data gathering to exploratory analysis and final conclusions — has largely been conducted using pen and paper methods. To help archivists analyze large-scale digital collections for archival purposes, we developed an interactive visual analytics application. The application narrows down different kinds of information about the collection, and presents them as meaningful data views. Multiple views and analysis features can be linked or unlinked on demand to enable researchers to compare and contrast different analyses, and to identify trends. We describe and present two user scenarios to show how the application allowed archivists to learn about a collection with accuracy, facilitated decision-making, and helped them arrive at conclusions. Esteva, M. Jain, S.D. Jain, V. Xu, W. multiple views visual analytics VAST analytical models data mining data visualization image color analysis layout rendering (computer graphics) visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) archival analysis data curation digital collections visual analytics 2011 vast11--6102459 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) A visual navigation system for querying neural stem cell imaging data. Cellular biology deals with studying the behavior of cells. Current time-lapse imaging microscopes help us capture the progress of experiments at intervals that allow for understanding of the dynamic and kinematic behavior of the cells. On the other hand, these devices generate such massive amounts of data (250GB of data per experiment) that manual sieving of data to identify interesting patterns becomes virtually impossible. In this paper we propose an end-to-end system to analyze time-lapse images of the cultures of human neural stem cells (hNSC), that includes an image processing system to analyze the images to extract all the relevant geometric and statistical features within and between images, a database management system to manage and handle queries on the data, a visual analytic system to navigate through the data, and a visual query system to explore different relationships and correlations between the parameters. In each stage of the pipeline we make novel algorithmic and conceptual contributions, and the entire system design is motivated by many different yet unanswered exploratory questions pursued by our neurobiologist collaborators. With a few examples we show how such abstract biological queries can be analyzed and answered by our system. Cummings, B. Gopi, M. Kulkarni, I. Mistry, S.Y. database experiment navigation VAST cells (biology) data visualization image segmentation navigation semantics shape visualization 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) cell imaging data management exploration navigation neuroscience query processing stem cell segmentation tracking visual analytics 2011 vast11--6102460 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) A visual analytics process for maritime resource allocation and risk assessment. In this paper, we present our collaborative work with the U.S. Coast Guard's Ninth District and Atlantic Area Commands where we developed a visual analytics system to analyze historic response operations and assess the potential risks in the maritime environment associated with the hypothetical allocation of Coast Guard resources. The system includes linked views and interactive displays that enable the analysis of trends, patterns and anomalies among the U.S. Coast Guard search and rescue (SAR) operations and their associated sorties. Our system allows users to determine the potential change in risks associated with closing certain stations in terms of response time, potential lives and property lost and provides optimal direction as to the nearest available station. We provide maritime risk assessment tools that allow analysts to explore Coast Guard coverage for SAR operations and identify regions of high risk. The system also enables a thorough assessment of all SAR operations conducted by each Coast Guard station in the Great Lakes region. Our system demonstrates the effectiveness of visual analytics in analyzing risk within the maritime domain and is currently being used by analysts at the Coast Guard Atlantic Area. Ebert, D.S. Maciejewski, R. Malik, A. Maule, B. visual analytics VAST calendars data visualization decision making lakes risk management time factors visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) coast guard risk assessment visual analytics 2011 vast11--6102463 10/26/2011 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) A two-stage framework for designing visual analytics system in organizational environments. A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational users' decision-making and reasoning processes. The problem is, however, most domain analytical practices generally vary from organization to organization. This leads to diverse designs of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs. To alleviate these problems, we present a two-stage framework for informing the design of a visual analytics system. This design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on incorporating both general domain analysis processes as well as individual's analytical activities. We illustrate both stages and their design components through examples, and hope this framework will be useful for designing future visual analytics systems. We validate the soundness of our framework with two visual analytics systems, namely Entity Workspace [8] and PatViz [37]. Bier, E.A. Butkiewicz, T. Dou, W. Ribarsky, W. Wang, X. visual analytics VAST context human computer interaction measurement organizations system analysis and design visual analytics 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) HCI design theory visual analytics 2011 vast12--6400491 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) A correlative analysis process in a visual analytics environment. Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson's product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them. Ebert, D.S. Elmqvist, N. Huang, W. Jang, Y. Maciejewski, R. Malik, A. statistics visual analytics VAST correlation data visualization educational institutions market research spatiotemporal phenomena time series analysis visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) correlative analysis visual analytics 2012 vast12--6400556 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) AlVis: Situation awareness in the surveillance of road tunnels. In the surveillance of road tunnels, video data plays an important role for a detailed inspection and as an input to systems for an automated detection of incidents. In disaster scenarios like major accidents, however, the increased amount of detected incidents may lead to situations where human operators lose a sense of the overall meaning of that data, a problem commonly known as a lack of situation awareness. The primary contribution of this paper is a design study of AlVis, a system designed to increase situation awareness in the surveillance of road tunnels. The design of AlVis is based on a simplified tunnel model which enables an overview of the spatiotemporal development of scenarios in real-time. The visualization explicitly represents the present state, the history, and predictions of potential future developments. Concepts for situation-sensitive prioritization of information ensure scalability from normal operation to major disaster scenarios. The visualization enables an intuitive access to live and historic video for any point in time and space. We illustrate AlVis by means of a scenario and report qualitative feedback by tunnel experts and operators. This feedback suggests that AlVis is suitable to save time in recognizing dangerous situations and helps to maintain an overview in complex disaster scenarios. Benedik, R. Buchetics, M. Piringer, H. awareness design study history overview VAST cameras context data visualization roads surveillance vehicles visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400493 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) An adaptive parameter space-filling algorithm for highly interactive cluster exploration. For a user to perceive continuous interactive response time in a visualization tool, the rule of thumb is that it must process, deliver, and display rendered results for any given interaction in under 100 milliseconds. In many visualization systems, successive interactions trigger independent queries and caching of results. Consequently, computationally expensive queries like multidimensional clustering cannot keep up with rapid sequences of interactions, precluding visual benefits such as motion parallax. In this paper, we describe a heuristic prefetching technique to improve the interactive response time of KMeans clustering in dynamic query visualizations of multidimensional data. We address the tradeoff between high interaction and intense query computation by observing how related interactions on overlapping data subsets produce similar clustering results, and characterizing these similarities within a parameter space of interaction. We focus on the two-dimensional parameter space defined by the minimum and maximum values of a time range manipulated by dragging and stretching a one-dimensional filtering lens over a plot of time series data. Using calculation of nearest neighbors of interaction points in parameter space, we reuse partial query results from prior interaction sequences to calculate both an immediate best-effort clustering result and to schedule calculation of an exact result. The method adapts to user interaction patterns in the parameter space by reprioritizing the interaction neighbors of visited points in the parameter space. A performance study on Mesonet meteorological data demonstrates that the method is a significant improvement over the baseline scheme in which interaction triggers on-demand, exact-range clustering with LRU caching. We also present initial evidence that approximate, temporary clustering results are sufficiently accurate (compared to exact results) to convey useful cluster structure during rapid and protracted interaction. Ahmed, Z. Weaver, C. cluster clustering dynamic query interaction time series VAST algorithm design and analysis clustering algorithms data visualization lenses prefetching time series analysis visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400559 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Analyst's Workspace: An embodied sensemaking environment for large, high-resolution displays. Distributed cognition and embodiment provide compelling models for how humans think and interact with the environment. Our examination of the use of large, high-resolution displays from an embodied perspective has lead directly to the development of a new sensemaking environment called Analyst's Workspace (AW). AW leverages the embodied resources made more accessible through the physical nature of the display to create a spatial workspace. By combining spatial layout of documents and other artifacts with an entity-centric, explorative investigative approach, AW aims to allow the analyst to externalize elements of the sensemaking process as a part of the investigation, integrated into the visual representations of the data itself. In this paper, we describe the various capabilities of AW and discuss the key principles and concepts underlying its design, emphasizing unique design principles for designing visual analytic tools for large, high-resolution displays. Andrews, C. North, C. cognition sensemaking VAST cognition humans keyboards monitoring navigation organizations visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) distributed cognition embodiment highresolution display large sensemaking space 2012 vast12--6400486 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Dis-function: Learning distance functions interactively. The world's corpora of data grow in size and complexity every day, making it increasingly difficult for experts to make sense out of their data. Although machine learning offers algorithms for finding patterns in data automatically, they often require algorithm-specific parameters, such as an appropriate distance function, which are outside the purview of a domain expert. We present a system that allows an expert to interact directly with a visual representation of the data to define an appropriate distance function, thus avoiding direct manipulation of obtuse model parameters. Adopting an iterative approach, our system first assumes a uniformly weighted Euclidean distance function and projects the data into a two-dimensional scatterplot view. The user can then move incorrectly-positioned data points to locations that reflect his or her understanding of the similarity of those data points relative to the other data points. Based on this input, the system performs an optimization to learn a new distance function and then re-projects the data to redraw the scatter-plot. We illustrate empirically that with only a few iterations of interaction and optimization, a user can achieve a scatterplot view and its corresponding distance function that reflect the user's knowledge of the data. In addition, we evaluate our system to assess scalability in data size and data dimension, and show that our system is computationally efficient and can provide an interactive or near-interactive user experience. Brodley, C.E. Brown, E.T. Chang, R. Liu, J. interaction machine learning scatterplot VAST bars data visualization euclidean distance machine learning vectors yttrium 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400489 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) iLAMP: Exploring high-dimensional spacing through backward multidimensional projection. Ever improving computing power and technological advances are greatly augmenting data collection and scientific observation. This has directly contributed to increased data complexity and dimensionality, motivating research of exploration techniques for multidimensional data. Consequently, a recent influx of work dedicated to techniques and tools that aid in understanding multidimensional datasets can be observed in many research fields, including biology, engineering, physics and scientific computing. While the effectiveness of existing techniques to analyze the structure and relationships of multidimensional data varies greatly, few techniques provide flexible mechanisms to simultaneously visualize and actively explore high-dimensional spaces. In this paper, we present an inverse linear affine multidimensional projection, coined iLAMP, that enables a novel interactive exploration technique for multidimensional data. iLAMP operates in reverse to traditional projection methods by mapping low-dimensional information into a high-dimensional space. This allows users to extrapolate instances of a multidimensional dataset while exploring a projection of the data to the planar domain. We present experimental results that validate iLAMP, measuring the quality and coherence of the extrapolated data; as well as demonstrate the utility of iLAMP to hypothesize the unexplored regions of a high-dimensional space. Brazil, E.V. Daniels, J. Joia, P. Nonato, L.G. Sousa, M.C. dos Santos Amorim, E.P. VAST data visualization measurement optimization robustness space exploration vectors visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400492 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Inter-active learning of ad-hoc classifiers for video visual analytics. Learning of classifiers to be used as filters within the analytical reasoning process leads to new and aggravates existing challenges. Such classifiers are typically trained ad-hoc, with tight time constraints that affect the amount and the quality of annotation data and, thus, also the users' trust in the classifier trained. We approach the challenges of ad-hoc training by inter-active learning, which extends active learning by integrating human experts' background knowledge to greater extent. In contrast to active learning, not only does inter-active learning include the users' expertise by posing queries of data instances for labeling, but it also supports the users in comprehending the classifier model by visualization. Besides the annotation of manually or automatically selected data instances, users are empowered to directly adjust complex classifier models. Therefore, our model visualization facilitates the detection and correction of inconsistencies between the classifier model trained by examples and the user's mental model of the class definition. Visual feedback of the training process helps the users assess the performance of the classifier and, thus, build up trust in the filter created. We demonstrate the capabilities of inter-active learning in the domain of video visual analytics and compare its performance with the results of random sampling and uncertainty sampling of training sets. Heidemann, G. Hoferlin, B. Hoferlin, M. Netzel, R. Weiskopf, D. filter uncertainty visual analytics VAST analytical models data models data visualization humans labeling training visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400487 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. We introduce the concept of just-in-time descriptive analytics as a novel application of computational and statistical techniques performed at interaction-time to help users easily understand the structure of data as seen in visualizations. Fundamental to just-intime descriptive analytics is (a) identifying visual features, such as clusters, outliers, and trends, user might observe in visualizations automatically, (b) determining the semantics of such features by performing statistical analysis as the user is interacting, and (c) enriching visualizations with annotations that not only describe semantics of visual features but also facilitate interaction to support high-level understanding of data. In this paper, we demonstrate just-in-time descriptive analytics applied to a point-based multi-dimensional visualization technique to identify and describe clusters, outliers, and trends. We argue that it provides a novel user experience of computational techniques working alongside of users allowing them to build faster qualitative mental models of data by demonstrating its application on a few use-cases. Techniques used to facilitate just-in-time descriptive analytics are described in detail along with their runtime performance characteristics. We believe this is just a starting point and much remains to be researched, as we discuss open issues and opportunities in improving accessibility and collaboration. Kandogan, E. collaboration interaction VAST clustering algorithms data mining data visualization feature extraction market research semantics visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) feature identification and characterization just-in-time descriptive analytics point-based visualizations 2012 vast12--6400485 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) LeadLine: Interactive visual analysis of text data through event identification and exploration. Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data. Dou, W. Ribarsky, W. Skau, D. Wang, X. Zhou, M.X. social text visual analytics VAST crawlers data mining event detection lead time series analysis twitter visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400484 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Relative N-gram signatures: Document visualization at the level of character N-grams. The Common N-Gram (CNG) classifier is a text classification algorithm based on the comparison of frequencies of character n-grams (strings of characters of length n) that are the most common in the considered documents and classes of documents. We present a text analytic visualization system that employs the CNG approach for text classification and uses the differences in frequency values of common n-grams in order to visually compare documents at the sub-word level. The visualization method provides both an insight into n-gram characteristics of documents or classes of documents and a visual interpretation of the workings of the CNG classifier. Jankowska, M. Keselj, V. Milios, E. document insight text VAST color context data visualization electronic mail frequency measurement visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) text classification visual analytics visual text analysis 2012 vast12--6400555 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Smart super views &#x2014; A knowledge-assisted interface for medical visualization. Due to the ever growing volume of acquired data and information, users have to be constantly aware of the methods for their exploration and for interaction. Of these, not each might be applicable to the data at hand or might reveal the desired result. Owing to this, innovations may be used inappropriately and users may become skeptical. In this paper we propose a knowledge-assisted interface for medical visualization, which reduces the necessary effort to use new visualization methods, by providing only the most relevant ones in a smart way. Consequently, we are able to expand such a system with innovations without the users to worry about when, where, and especially how they may or should use them. We present an application of our system in the medical domain and give qualitative feedback from domain experts. Baclija, I. Bouzari, H. Bruckner, S. Gröller, M.E. Kochl, A. Mistelbauer, G. Schernthaner, R. Sramek, M. interaction VAST biomedical imaging bones data visualization electronic mail fuzzy logic pragmatics semantics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) fuzzy logic interaction visualization 2012 vast12--6400558 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization. Increasingly, social network datasets contain social attribute information about actors and their relationship. Analyzing such network with social attributes requires making sense of not only its structural features, but also the relationship between social features in attributes and network structures. Existing social network analysis tools are usually weak in supporting complex analytical tasks involving both structural and social features, and often overlook users' needs for sensemaking tools that help to gather, synthesize, and organize information of these features. To address these challenges, we propose a sensemaking framework of social-network visual analytics in this paper. This framework considers both bottom-up processes, which are about constructing new understandings based on collected information, and top-down processes, which concern using prior knowledge to guide information collection, in analyzing social networks from both social and structural perspectives. The framework also emphasizes the externalization of sensemaking processes through interactive visualization. Guided by the framework, we develop a system, SocialNetSense, to support the sensemaking in visual analytics of social networks with social attributes. The example of using our system to analyze a scholar collaboration network shows that our approach can help users gain insight into social networks both structurally and socially, and enhance their process awareness in visual analytics. Anderson, P.F. Gou, L. Luo, A. Zhang, X. awareness collaboration insight network sensemaking social visual analytics VAST collaboration data visualization educational institutions history social network services visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) sensemaking social network socialnetsense visual analytics visualization 2012 vast12--6400557 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. Recent advances in technology have enabled social media services to support space-time indexed data, and internet users from all over the world have created a large volume of time-stamped, geo-located data. Such spatiotemporal data has immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. In analyzing social media data, researchers have mainly focused on finding temporal trends according to volume-based importance. Hence, a relatively small volume of relevant messages may easily be obscured by a huge data set indicating normal situations. In this paper, we present a visual analytics approach that provides users with scalable and interactive social media data analysis and visualization including the exploration and examination of abnormal topics and events within various social media data sources, such as Twitter, Flickr and YouTube. In order to find and understand abnormal events, the analyst can first extract major topics from a set of selected messages and rank them probabilistically using Latent Dirichlet Allocation. He can then apply seasonal trend decomposition together with traditional control chart methods to find unusual peaks and outliers within topic time series. Our case studies show that situational awareness can be improved by incorporating the anomaly and trend examination techniques into a highly interactive visual analysis process. Bosch, H. Chae, J. Ebert, D.S. Ertl, T. Jang, Y. Maciejewski, R. Thom, D. awareness social time series visual analytics VAST data mining earthquakes educational institutions media spatiotemporal phenomena time series analysis twitter 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) relevance feedback 2012 vast12--6400488 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Subspace search and visualization to make sense of alternative clusterings in high-dimensional data. In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive methods aim to provide effective visual mappings to show, relate, and navigate HD data. Furthermore, almost all of these methods conduct the analysis from a singular perspective, meaning that they consider the data in either the original HD data space, or a reduced version thereof. Additionally, HD data spaces often consist of combined features that measure different properties, in which case the particular relationships between the various properties may not be clear to the analysts a priori since it can only be revealed if appropriate feature combinations (subspaces) of the data are taken into consideration. Considering just a single subspace is, however, often not sufficient since different subspaces may show complementary, conjointly, or contradicting relations between data items. Useful information may consequently remain embedded in sets of subspaces of a given HD input data space. Relying on the notion of subspaces, we propose a novel method for the visual analysis of HD data in which we employ an interestingness-guided subspace search algorithm to detect a candidate set of subspaces. Based on appropriately defined subspace similarity functions, we visualize the subspaces and provide navigation facilities to interactively explore large sets of subspaces. Our approach allows users to effectively compare and relate subspaces with respect to involved dimensions and clusters of objects. We apply our approach to synthetic and real data sets. We thereby demonstrate its support for understanding HD data from different perspectives, effectively yielding a more complete view on HD data. Bertini, E. Farber, I. Keim, D.A. Maas, F. Schreck, T. Seidl, T. Tatu, A. cluster high-dimensional data navigation VAST algorithm design and analysis clustering algorithms data visualization educational institutions high definition video topology visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) display algorithms 2012 vast12--6400560 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) The Deshredder: A visual analytic approach to reconstructing shredded documents. Reconstruction of shredded documents remains a significant challenge. Creating a better document reconstruction system enables not just recovery of information accidentally lost but also understanding our limitations against adversaries' attempts to gain access to information. Existing approaches to reconstructing shredded documents adopt either a predominantly manual (e.g., crowd-sourcing) or a near automatic approach. We describe Deshredder, a visual analytic approach that scales well and effectively incorporates user input to direct the reconstruction process. Deshredder represents shredded pieces as time series and uses nearest neighbor matching techniques that enable matching both the contours of shredded pieces as well as the content of shreds themselves. More importantly, Deshred-der's interface support visual analytics through user interaction with similarity matrices as well as higher level assembly through more complex stitching functions. We identify a functional task taxonomy leading to design considerations for constructing deshredding solutions, and describe how Deshredder applies to problems from the DARPA Shredder Challenge through expert evaluations. Butler, P. Chakraborty, P. Ramakrishan, N. document interaction taxonomy time series visual analytics VAST assembly humans image reconstruction shape time series analysis visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400554 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analytics for the big data era &#x2014; A comparative review of state-of-the-art commercial systems. Visual analytics (VA) system development started in academic research institutions where novel visualization techniques and open source toolkits were developed. Simultaneously, small software companies, sometimes spin-offs from academic research institutions, built solutions for specific application domains. In recent years we observed the following trend: some small VA companies grew exponentially; at the same time some big software vendors such as IBM and SAP started to acquire successful VA companies and integrated the acquired VA components into their existing frameworks. Generally the application domains of VA systems have broadened substantially. This phenomenon is driven by the generation of more and more data of high volume and complexity, which leads to an increasing demand for VA solutions from many application domains. In this paper we survey a selection of state-of-the-art commercial VA frameworks, complementary to an existing survey on open source VA tools. From the survey results we identify several improvement opportunities as future research directions. Behrisch, M. Keim, D.A. Last, H. Mittelstadt, S. Pompl, R. Schreck, T. Stoffel, A. Weber, S. Zhang, L. visual analytics VAST analytical models bismuth data handling data models data visualization software visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400553 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analytics methods for categoric spatio-temporal data. We focus on visual analysis of space- and time-referenced categorical data, which describe possible states of spatial (geographical) objects or locations and their changes over time. The analysis of these data is difficult as there are only limited possibilities to analyze the three aspects (location, time and category) simultaneously. We present a new approach which interactively combines (a) visualization of categorical changes over time; (b) various spatial data displays; (c) computational techniques for task-oriented selection of time steps. They provide an expressive visualization with regard to either the overall evolution over time or unusual changes. We apply our approach on two use cases demonstrating its usefulness for a wide variety of tasks. We analyze data from movement tracking and meteorologic areas. Using our approach, expected events could be detected and new insights were gained. Andrienko, G. Andrienko, N. Bremm, S. Tekusova, M. von Landesberger, T. categorical visual analytics VAST algorithm design and analysis data analysis data visualization electronic mail image color analysis time series analysis visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400494 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual cluster exploration of web clickstream data. Web clickstream data are routinely collected to study how users browse the web or use a service. It is clear that the ability to recognize and summarize user behavior patterns from such data is valuable to e-commerce companies. In this paper, we introduce a visual analytics system to explore the various user behavior patterns reflected by distinct clickstream clusters. In a practical analysis scenario, the system first presents an overview of clickstream clusters using a Self-Organizing Map with Markov chain models. Then the analyst can interactively explore the clusters through an intuitive user interface. He can either obtain summarization of a selected group of data or further refine the clustering result. We evaluated our system using two different datasets from eBay. Analysts who were working on the same data have confirmed the system's effectiveness in extracting user behavior patterns from complex datasets and enhancing their ability to reason. Ma, K.-L. Shen, Z. Sundaresan, N. Wei, J. cluster clustering overview visual analytics VAST data models data visualization hidden markov models layout markov processes prototypes visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) 2012 vast12--6400490 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual pattern discovery using random projections. An essential element of exploratory data analysis is the use of revealing low-dimensional projections of high-dimensional data. Projection Pursuit has been an effective method for finding interesting low-dimensional projections of multidimensional spaces by optimizing a score function called a projection pursuit index. However, the technique is not scalable to high-dimensional spaces. Here, we introduce a novel method for discovering noteworthy views of high-dimensional data spaces by using binning and random projections. We define score functions, akin to projection pursuit indices, that characterize visual patterns of the low-dimensional projections that constitute feature subspaces. We also describe an analytic, multivariate visualization platform based on this algorithm that is scalable to extremely large problems. Anand, A. Dang, T.N. Wilkinson, L. high-dimensional data VAST data mining data visualization indexes manifolds vectors visual analytics 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) high-dimensional data random projections 2012 vast12--6400552 10/16/2012 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) Watch this: A taxonomy for dynamic data visualization. Visualizations embody design choices about data access, data transformation, visual representation, and interaction. To interpret a static visualization, a person must identify the correspondences between the visual representation and the underlying data. These correspondences become moving targets when a visualization is dynamic. Dynamics may be introduced in a visualization at any point in the analysis and visualization process. For example, the data itself may be streaming, shifting subsets may be selected, visual representations may be animated, and interaction may modify presentation. In this paper, we focus on the impact of dynamic data. We present a taxonomy and conceptual framework for understanding how data changes influence the interpretability of visual representations. Visualization techniques are organized into categories at various levels of abstraction. The salient characteristics of each category and task suitability are discussed through examples from the scientific literature and popular practices. Examining the implications of dynamically updating visualizations warrants attention because it directly impacts the interpretability (and thus utility) of visualizations. The taxonomy presented provides a reference point for further exploration of dynamic data visualization techniques. Cottam, J.A. Lumsdaine, A. Weaver, C. interaction taxonomy VAST context data visualization encoding image color analysis retina taxonomy visualization 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) dynamic data interpretation 2012 vast12--254 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data. Contingency tables summarize the relations between categorical variables and arise in both scientific and business domains. Asymmetrically large two-way contingency tables pose a problem for common visualization methods. The Contingency Wheel has been recently proposed as an interactive visual method to explore and analyze such tables. However, the scalability and readability of this method are limited when dealing with large and dense tables. In this paper we present Contingency Wheel++, new visual analytics methods that overcome these major shortcomings: (1) regarding automated methods, a measure of association based on Pearson's residuals alleviates the bias of the raw residuals originally used, (2) regarding visualization methods, a frequency-based abstraction of the visual elements eliminates overlapping and makes analyzing both positive and negative associations possible, and (3) regarding the interactive exploration environment, a multi-level overview+detail interface enables exploring individual data items that are aggregated in the visualization or in the table using coordinated views. We illustrate the applicability of these new methods with a use case and show how they enable discovering and analyzing nontrivial patterns and associations in large categorical data. Aigner, W. Alsallakh, B. Gröller, M.E. Miksch, S. business categorical coordinated views overview visual analytics VAST data visualization frequency measurement histograms motion pictures visual analytics IEEE Transactions on Visualization and Computer Graphics contingency table analysis information interfaces and representation large categorical data visual analytics 2012 vast12--191 10/16/2012 IEEE Transactions on Visualization and Computer Graphics A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series. We present a Visual Analytics approach that addresses the detection of interesting patterns in numerical time series, specifically from environmental sciences. Crucial for the detection of interesting temporal patterns are the time scale and the starting points one is looking at. Our approach makes no assumption about time scale and starting position of temporal patterns and consists of three main steps: an algorithm to compute statistical values for all possible time scales and starting positions of intervals, visual identification of potentially interesting patterns in a matrix visualization, and interactive exploration of detected patterns. We demonstrate the utility of this approach in two scientific scenarios and explain how it allowed scientists to gain new insight into the dynamics of environmental systems. Dransch, D. Hege, H.-C. Kothur, P. Sips, M. Unger, A. insight matrix time series visual analytics VAST data visualization earth entropy meteorology time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics multiscale visualization time series analysis visual analytics 2012 vast12--195 10/16/2012 IEEE Transactions on Visualization and Computer Graphics An Affordance-Based Framework for Human Computation and Human-Computer Collaboration. Visual Analytics is "the science of analytical reasoning facilitated by visual interactive interfaces" [70]. The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on humanand machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field. Chang, R. Crouser, R.J. collaboration interaction overview visual analytics VAST cognition computation theory human factors resource management visual analytics IEEE Transactions on Visualization and Computer Graphics framework human complexity human computation theory 2012 vast12--219 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Enterprise Data Analysis and Visualization: An Interview Study. Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform business decisions, and combat fraud. Though numerous analysis and visualization tools have been built to improve the scale and efficiency at which analysts can work, there has been little research on how analysis takes place within the social and organizational context of companies. To better understand the enterprise analysts' ecosystem, we conducted semi-structured interviews with 35 data analysts from 25 organizations across a variety of sectors, including healthcare, retail, marketing and finance. Based on our interview data, we characterize the process of industrial data analysis and document how organizational features of an enterprise impact it. We describe recurring pain points, outstanding challenges, and barriers to adoption for visual analytic tools. Finally, we discuss design implications and opportunities for visual analysis research. Heer, J. Hellerstein, J.M. Kandel, S. Paepcke, A. business document social VAST collaboration computer hacking data visualization distributed databases organizations IEEE Transactions on Visualization and Computer Graphics analysis data enterprise visualization 2012 vast12--224 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts. While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems. Kang, Y. Stasko, J. evaluation sensemaking visual analytics VAST data visualization electronic mail market research qualitative analysis visual analytics IEEE Transactions on Visualization and Computer Graphics case study qualitative evaluation visual analytics 2012 vast12--258 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Scatter/Gather Clustering: Flexibly Incorporating User Feedback to Steer Clustering Results. Significant effort has been devoted to designing clustering algorithms that are responsive to user feedback or that incorporate prior domain knowledge in the form of constraints. However, users desire more expressive forms of interaction to influence clustering outcomes. In our experiences working with diverse application scientists, we have identified an interaction style scatter/gather clustering that helps users iteratively restructure clustering results to meet their expectations. As the names indicate, scatter and gather are dual primitives that describe whether clusters in a current segmentation should be broken up further or, alternatively, brought back together. By combining scatter and gather operations in a single step, we support very expressive dynamic restructurings of data. Scatter/gather clustering is implemented using a nonlinear optimization framework that achieves both locality of clusters and satisfaction of user-supplied constraints. We illustrate the use of our scatter/gather clustering approach in a visual analytic application to study baffle shapes in the bat biosonar (ears and nose) system. We demonstrate how domain experts are adept at supplying scatter/gather constraints, and how our framework incorporates these constraints effectively without requiring numerous instance-level constraints. Grimm, C. Hossain, M.S. Muller, R. Ojili, P.K.R. Ramakrishnan, N. Watson, L.T. clustering interaction VAST algorithm design and analysis clustering algorithms computer science linear programming optimization visual analytics IEEE Transactions on Visualization and Computer Graphics alternative clustering constrained clustering scatter/gather clustering 2012 vast12--260 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering. Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition. Endert, A. Fiaux, P. North, C. clustering document interaction sensemaking text user study VAST analytical models mathematical model semantics user interfaces visual analytics IEEE Transactions on Visualization and Computer Graphics analytic reasoning sensemaking user interaction visual analytics visualization 2012 vast12--273 10/16/2012 IEEE Transactions on Visualization and Computer Graphics The User Puzzle&#8212;Explaining the Interaction with Visual Analytics Systems. Visual analytics emphasizes the interplay between visualization, analytical procedures performed by computers and human perceptual and cognitive activities. Human reasoning is an important element in this context. There are several theories in psychology and HCI explaining open-ended and exploratory reasoning. Five of these theories (sensemaking theories, gestalt theories, distributed cognition, graph comprehension theories and skill-rule-knowledge models) are described in this paper. We discuss their relevance for visual analytics. In order to do this more systematically, we developed a schema of categories relevant for visual analytics research and evaluation. All these theories have strengths but also weaknesses in explaining interaction with visual analytics systems. A possibility to overcome the weaknesses would be to combine two or more of these theories. Mayr, E. Pohl, M. Smuc, M. cognition evaluation graph interaction sensemaking visual analytics VAST cognition human factors problem-solving psychology visual analytics IEEE Transactions on Visualization and Computer Graphics cognitive theory interaction design problem solving reasoning visual knowledge discovery 2012 vast12--276 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visual Analytics Methodology for Eye Movement Studies. Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks. Andrienko, G. Andrienko, N. Burch, M. Weiskopf, D. evaluation geographic visual analytics VAST data visualization eye standards tracking trajectory visual analytics IEEE Transactions on Visualization and Computer Graphics eye tracking movement data trajectory analysis visual analytics 2012 vast12--277 10/16/2012 IEEE Transactions on Visualization and Computer Graphics Visual Classifier Training for Text Document Retrieval. Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora. Bosch, H. Ertl, T. Heimerl, F. Koch, S. document filter machine learning text user study VAST classification human computer interaction information retrieval learning systems performance evaluation training data visual analytics IEEE Transactions on Visualization and Computer Graphics active learning classification human computer interaction information retrieval user evaluation visual analytics 2012 vast13--125 10/16/2013 IEEE Transactions on Visualization and Computer Graphics A Partition-Based Framework for Building and Validating Regression Models. Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models. Muhlbacher, T. Piringer, H. case study categorical VAST complexity theory computational modeling feature extraction frequency-domain analysis modeling regression analysis IEEE Transactions on Visualization and Computer Graphics data partitioning feature selection guided visualization model building regression visual knowledge discovery 2013 vast13--132 10/16/2013 IEEE Transactions on Visualization and Computer Graphics An Extensible Framework for Provenance in Human Terrain Visual Analytics. We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework. Dykes, J. Nguyen, P.H. Slingsby, A. Stephens, D. Walker, R. Wong, B.L.W. Wood, J. Xu, K. Zheng, Y. visual analytics VAST context awareness data visualization human factors terrain mapping visual analytics IEEE Transactions on Visualization and Computer Graphics bookmarks framework human terrain analysis narratives provenance 2013 vast13--146 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Decision Exploration Lab: A Visual Analytics Solution for Decision Management. We pesent a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry. Baudel, T. Broeksema, B. Crisafulli, P. Telea, A.C. business insight visual analytics VAST analytical models data visualization decision making statistical analysis visual analytics IEEE Transactions on Visualization and Computer Graphics decision support systems model validation and analysis multivariate statistics program analysis 2013 vast13--157 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Explainers: Expert Explorations with Crafted Projections. This paper introduces an approach to exploration and discovery in high-dimensional data that incorporates a user's knowledge and questions to craft sets of projection functions meaningful to them. Unlike most prior work that defines projections based on their statistical properties, our approach creates projection functions that align with user-specified annotations. Therefore, the resulting derived dimensions represent concepts defined by the user's examples. These especially crafted projection functions, or explainers, can help find and explain relationships between the data variables and user-designated concepts. They can organize the data according to these concepts. Sets of explainers can provide multiple perspectives on the data. Our approach considers tradeoffs in choosing these projection functions, including their simplicity, expressive power, alignment with prior knowledge, and diversity. We provide techniques for creating collections of explainers. The methods, based on machine learning optimization frameworks, allow exploring the tradeoffs. We demonstrate our approach on model problems and applications in text analysis. Gleicher, M. high-dimensional data machine learning text VAST cities and towns optimization quantization (signal) support vector machines text mining IEEE Transactions on Visualization and Computer Graphics exploration high-dimensional spaces support vector machines 2013 vast13--162 10/16/2013 IEEE Transactions on Visualization and Computer Graphics HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies. Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system - HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics. Dou, W. Ma, Z. Ribarsky, W. Wang, X. Yu, L. case study hierarchies hierarchy navigation text user study visual analytics VAST algorithm design and analysis analytical models computational modeling text mining visual analytics vocabulary IEEE Transactions on Visualization and Computer Graphics hierarchical topic representation rose tree topic modeling visual analytics 2013 vast13--164 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Identifying Redundancy and Exposing Provenance in Crowdsourced Data Analysis. We present a system that lets analysts use paid crowd workers to explore data sets and helps analysts interactively examine and build upon workers' insights. We take advantage of the fact that, for many types of data, independent crowd workers can readily perform basic analysis tasks like examining views and generating explanations for trends and patterns. However, workers operating in parallel can often generate redundant explanations. Moreover, because workers have different competencies and domain knowledge, some responses are likely to be more plausible than others. To efficiently utilize the crowd's work, analysts must be able to quickly identify and consolidate redundant responses and determine which explanations are the most plausible. In this paper, we demonstrate several crowd-assisted techniques to help analysts make better use of crowdsourced explanations: (1) We explore crowd-assisted strategies that utilize multiple workers to detect redundant explanations. We introduce color clustering with representative selection-a strategy in which multiple workers cluster explanations and we automatically select the most-representative result-and show that it generates clusterings that are as good as those produced by experts. (2) We capture explanation provenance by introducing highlighting tasks and capturing workers' browsing behavior via an embedded web browser, and refine that provenance information via source-review tasks. We expose this information in an explanation-management interface that allows analysts to interactively filter and sort responses, select the most plausible explanations, and decide which to explore further. Agrawala, M. Ginosar, S. Hartmann, B. Steinitz, A. Willett, W. cluster clustering color filter VAST clustering algorithms data analysis image color analysis market research redundancy social network services IEEE Transactions on Visualization and Computer Graphics crowdsourcing social data analysis 2013 vast13--167 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets. Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique which provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature. Balakrishnan, R. Chevalier, F. Collins, C. Zhao, J. interaction sensemaking VAST data visualization faceted searches information filters market research IEEE Transactions on Visualization and Computer Graphics dynamic query faceted browsing information visualization interaction network exploration visual analytics 2013 vast13--168 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Interactive Exploration of Surveillance Video through Action Shot Summarization and Trajectory Visualization. We propose a novel video visual analytics system for interactive exploration of surveillance video data. Our approach consists of providing analysts with various views of information related to moving objects in a video. To do this we first extract each object's movement path. We visualize each movement by (a) creating a single action shot image (a still image that coalesces multiple frames), (b) plotting its trajectory in a space-time cube and (c) displaying an overall timeline view of all the movements. The action shots provide a still view of the moving object while the path view presents movement properties such as speed and location. We also provide tools for spatial and temporal filtering based on regions of interest. This allows analysts to filter out large amounts of movement activities while the action shot representation summarizes the content of each movement. We incorporated this multi-part visual representation of moving objects in sViSIT, a tool to facilitate browsing through the video content by interactive querying and retrieval of data. Based on our interaction with security personnel who routinely interact with surveillance video data, we identified some of the most common tasks performed. This resulted in designing a user study to measure time-to-completion of the various tasks. These generally required searching for specific events of interest (targets) in videos. Fourteen different tasks were designed and a total of 120 min of surveillance video were recorded (indoor and outdoor locations recording movements of people and vehicles). The time-to-completion of these tasks were compared against a manual fast forward video browsing guided with movement detection. We demonstrate how our system can facilitate lengthy video exploration and significantly reduce browsing time to find events of interest. Reports from expert users identify positive aspects of our approach which we summarize in our recommendations for future video visual analytics systems. Irani, P.P. Meghdadi, A.H. filter interaction security user study visual analytics VAST data visualization image segmentation interactive states navigation surveillance tracking visual analytics IEEE Transactions on Visualization and Computer Graphics surveillance video video browsing and exploration video summarization video visual analytics video visualization 2013 vast13--178 10/16/2013 IEEE Transactions on Visualization and Computer Graphics MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation. We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work. Bernard, J. Kohlhammer, J. Kruger, B. May, T. Schreck, T. Wilhelm, N. animation collaboration design study field study navigation overview time series VAST data collection data visualization databases time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics cluster glyph data aggregation exploratory search motion capture data multivariate time series visual analytics 2013 vast13--181 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Open-Box Spectral Clustering: Applications to Medical Image Analysis. Spectral clustering is a powerful and versatile technique, whose broad range of applications includes 3D image analysis. However, its practical use often involves a tedious and time-consuming process of tuning parameters and making application-specific choices. In the absence of training data with labeled clusters, help from a human analyst is required to decide the number of clusters, to determine whether hierarchical clustering is needed, and to define the appropriate distance measures, parameters of the underlying graph, and type of graph Laplacian. We propose to simplify this process via an open-box approach, in which an interactive system visualizes the involved mathematical quantities, suggests parameter values, and provides immediate feedback to support the required decisions. Our framework focuses on applications in 3D image analysis, and links the abstract high-dimensional feature space used in spectral clustering to the three-dimensional data space. This provides a better understanding of the technique, and helps the analyst predict how well specific parameter settings will generalize to similar tasks. In addition, our system supports filtering outliers and labeling the final clusters in such a way that user actions can be recorded and transferred to different data in which the same structures are to be found. Our system supports a wide range of inputs, including triangular meshes, regular grids, and point clouds. We use our system to develop segmentation protocols in chest CT and brain MRI that are then successfully applied to other datasets in an automated manner. Kindlmann, G.L. Schultz, T. clustering graph VAST clustering data visualization eigenvalues and eigenfunctions image analysis image segmentation laplace equations three-dimensional displays IEEE Transactions on Visualization and Computer Graphics high-dimensional embeddings image segmentation linked views programming with example spectral clustering 2013 vast13--186 10/16/2013 IEEE Transactions on Visualization and Computer Graphics ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering. The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios. Bosch, H. Ertl, T. Heimerl, F. Koch, S. Kruger, R. Puttmann, E. Thom, D. Worner, M. awareness filter VAST blogs information retrieval labeling real-time systems social network services spatiotemporal phenomena twitter IEEE Transactions on Visualization and Computer Graphics filter construction information visualization live monitoring microblog analysis query construction social media monitoring text analytics text classification twitter visual analytics 2013 vast13--188 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Semantics of Directly Manipulating Spatializations. When high-dimensional data is visualized in a 2D plane by using parametric projection algorithms, users may wish to manipulate the layout of the data points to better reflect their domain knowledge or to explore alternative structures. However, few users are well-versed in the algorithms behind the visualizations, making parameter tweaking more of a guessing game than a series of decisive interactions. Translating user interactions into algorithmic input is a key component of Visual to Parametric Interaction (V2PI) [13]. Instead of adjusting parameters, users directly move data points on the screen, which then updates the underlying statistical model. However, we have found that some data points that are not moved by the user are just as important in the interactions as the data points that are moved. Users frequently move some data points with respect to some other 'unmoved' data points that they consider as spatially contextual. However, in current V2PI interactions, these points are not explicitly identified when directly manipulating the moved points. We design a richer set of interactions that makes this context more explicit, and a new algorithm and sophisticated weighting scheme that incorporates the importance of these unmoved data points into V2PI. Bradel, L. House, L. Hu, X. Leman, S. Maiti, D. North, C. high-dimensional data interaction VAST algorithm design and analysis cognitive science data visualization mathematical model semantics IEEE Transactions on Visualization and Computer Graphics statistical models visual analytics visual to parametric interaction 2013 vast13--190 10/16/2013 IEEE Transactions on Visualization and Computer Graphics SketchPadN-D: WYDIWYG Sculpting and Editing in High-Dimensional Space. High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, SketchPadND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. SketchPadND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees that the data generated is truly high-dimensional. We demonstrate our tool's usefulness in real data visualization scenarios. Mueller, K. Ruchikachorn, P. Wang, B. high-dimensional data parallel coordinates VAST data visualization image color analysis shape analysis IEEE Transactions on Visualization and Computer Graphics data acquisition and management data editing high-dimensional data interaction multiple views multivariate data n-d navigation parallel coordinates scatterplot synthetic data generation user interface 2013 vast13--193 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Space Transformation for Understanding Group Movement. We suggest a methodology for analyzing movement behaviors of individuals moving in a group. Group movement is analyzed at two levels of granularity: the group as a whole and the individuals it comprises. For analyzing the relative positions and movements of the individuals with respect to the rest of the group, we apply space transformation, in which the trajectories of the individuals are converted from geographical space to an abstract 'group space'. The group space reference system is defined by both the position of the group center, which is taken as the coordinate origin, and the direction of the group's movement. Based on the individuals' positions mapped onto the group space, we can compare the behaviors of different individuals, determine their roles and/or ranks within the groups, and, possibly, understand how group movement is organized. The utility of the methodology has been evaluated by applying it to a set of real data concerning movements of wild social animals and discussing the results with experts in animal ethology. Andrienko, G. Andrienko, N. Barrett, L. Dostie, M. Henzi, P. social VAST behavioral science data models market research trajectory visual analytics IEEE Transactions on Visualization and Computer Graphics collective movement movement data visual analytics 2013 vast13--194 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Space-Time Visual Analytics of Eye-Tracking Data for Dynamic Stimuli. We introduce a visual analytics method to analyze eye movement data recorded for dynamic stimuli such as video or animated graphics. The focus lies on the analysis of data of several viewers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes can be analyzed in a static 3D representation. Shotbased, spatiotemporal clustering of the data generates potential areas of interest that can be filtered interactively. We also facilitate data drill-down: the gaze points are shown with density-based color mapping and individual scan paths as lines in the space-time cube. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data. Common eye-tracking visualization techniques are extended to incorporate the spatiotemporal characteristics of the data. For example, heat maps are extended to motion-compensated heat maps and trajectories of scan paths are included in the space-time visualization. Our visual analytics approach is assessed in a qualitative users study with expert users, which showed the usefulness of the approach and uncovered that the experts applied different analysis strategies supported by the system. Kurzhals, K. Weiskopf, D. clustering color coordinated views visual analytics VAST clustering algorithms context awareness data visualization space-time codes spatiotemporal phenomena tracking visual analytics IEEE Transactions on Visualization and Computer Graphics dynamic areas of interest eye-tracking motion-compensated heat map space-time cube spatiotemporal clustering 2013 vast13--197 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Supporting Awareness through Collaborative Brushing and Linking of Tabular Data. Maintaining an awareness of collaborators' actions is critical during collaborative work, including during collaborative visualization activities. Particularly when collaborators are located at a distance, it is important to know what everyone is working on in order to avoid duplication of effort, share relevant results in a timely manner and build upon each other's results. Can a person's brushing actions provide an indication of their queries and interests in a data set? Can these actions be revealed to a collaborator without substantially disrupting their own independent work? We designed a study to answer these questions in the context of distributed collaborative visualization of tabular data. Participants in our study worked independently to answer questions about a tabular data set, while simultaneously viewing brushing actions of a fictitious collaborator, shown directly within a shared workspace. We compared three methods of presenting the collaborator's actions: brushing & linking (i.e. highlighting exactly what the collaborator would see), selection (i.e. showing only a selected item), and persistent selection (i.e. showing only selected items but having them persist for some time). Our results demonstrated that persistent selection enabled some awareness of the collaborator's activities while causing minimal interference with independent work. Other techniques were less effective at providing awareness, and brushing & linking caused substantial interference. These findings suggest promise for the idea of exploiting natural brushing actions to provide awareness in collaborative work. Hajizadeh, A.H. Leung, R. Tory, M. awareness brushing VAST collaborative work context awareness data visualization IEEE Transactions on Visualization and Computer Graphics attentionally ambient visualization awareness brushing and linking collaboration linked views user study 2013 vast13--198 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Supporting the Visual Analysis of Dynamic Networks by Clustering associated Temporal Attributes. The visual analysis of dynamic networks is a challenging task. In this paper, we introduce a new approach supporting the discovery of substructures sharing a similar trend over time by combining computation, visualization and interaction. With existing techniques, their discovery would be a tedious endeavor because of the number of nodes, edges as well as time points to be compared. First, on the basis of the supergraph, we therefore group nodes and edges according to their associated attributes that are changing over time. Second, the supergraph is visualized to provide an overview of the groups of nodes and edges with similar behavior over time in terms of their associated attributes. Third, we provide specific interactions to explore and refine the temporal clustering, allowing the user to further steer the analysis of the dynamic network. We demonstrate our approach by the visual analysis of a large wireless mesh network. Cap, C.H. Hadlak, S. Schumann, H. Wollenberg, T. clustering interaction network overview VAST current measurement image color analysis market research power system dynamics time measurement time series analysis IEEE Transactions on Visualization and Computer Graphics dynamic networks supergraph clustering visualization 2013 vast13--200 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Temporal Event Sequence Simplification. Electronic Health Records (EHRs) have emerged as a cost-effective data source for conducting medical research. The difficulty in using EHRs for research purposes, however, is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets. Our previous work introduced EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As datasets become larger and more varied, however, it becomes increasingly difficult to provide a succinct, summarizing display. This paper presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Furthermore, we present a novel metric for measuring visual complexity, and a language for codifying disjoint strategies into an overarching simplification framework. These simplifications were used by real-world researchers to gain new and valuable insights from initially overwhelming datasets. Lan, R. Lee, H. Monroe, M. Plaisant, C. Shneiderman, B. VAST complexity theory data mining data visualization electronic medical records market research IEEE Transactions on Visualization and Computer Graphics electronic heath records event sequences simplification temporal query 2013 vast13--205 10/16/2013 IEEE Transactions on Visualization and Computer Graphics The Impact of Physical Navigation on Spatial Organization for Sensemaking. Spatial organization has been proposed as a compelling approach to externalizing the sensemaking process. However, there are two ways in which space can be provided to the user: by creating a physical workspace that the user can interact with directly, such as can be provided by a large, high-resolution display, or through the use of a virtual workspace that the user navigates using virtual navigation techniques such as zoom and pan. In this study we explicitly examined the use of spatial sensemaking techniques within these two environments. The results demonstrate that these two approaches to providing sensemaking space are not equivalent, and that the greater embodiment afforded by the physical workspace changes how the space is perceived and used, leading to increased externalization of the sensemaking process. Andrews, C. North, C. navigation sensemaking zoom VAST browsers image color analysis navigation visual analytics IEEE Transactions on Visualization and Computer Graphics embodiment large high-resolution displays physical navigation sensemaking visual analytics 2013 vast13--206 10/16/2013 IEEE Transactions on Visualization and Computer Graphics TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data. Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects. Aigner, W. Alsallakh, B. Lammarsch, T. Miksch, S. Rind, A. network visual analytics VAST data models data structures data visualization time-domain analysis visual analytics IEEE Transactions on Visualization and Computer Graphics information visualization software infrastructure temporal data time toolkits visual analytics 2013 vast13--207 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop. Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. Bown, R. Chen, M. Chung, D.H.S. Griffiths, I.W. Jones, M.W. Legg, P.A. Parry, M.L. interaction machine learning visual analytics VAST analytical models computational modeling data visualization machine learning multimedia communication visual analytics IEEE Transactions on Visualization and Computer Graphics data clustering machine learning multimedia visualization visual knowledge discovery 2013 vast13--212 10/16/2013 IEEE Transactions on Visualization and Computer Graphics UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization. Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets. Choo, J. Lee, C. Park, H. Reddy, C.K. document matrix text visual analytics VAST analytical models computational modeling context modeling interactive states visual analytics IEEE Transactions on Visualization and Computer Graphics interactive clustering latent dirichlet allocation nonnegative matrix factorization text analytics topic modeling visual analytics 2013 vast13--213 10/16/2013 IEEE Transactions on Visualization and Computer Graphics VAICo: Visual Analysis for Image Comparison. Scientists, engineers, and analysts are confronted with ever larger and more complex sets of data, whose analysis poses special challenges. In many situations it is necessary to compare two or more datasets. Hence there is a need for comparative visualization tools to help analyze differences or similarities among datasets. In this paper an approach for comparative visualization for sets of images is presented. Well-established techniques for comparing images frequently place them side-by-side. A major drawback of such approaches is that they do not scale well. Other image comparison methods encode differences in images by abstract parameters like color. In this case information about the underlying image data gets lost. This paper introduces a new method for visualizing differences and similarities in large sets of images which preserves contextual information, but also allows the detailed analysis of subtle variations. Our approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this process are then presented in an interactive web application which allows users to rapidly explore the space of differences and drill-down on particular features. We demonstrate the flexibility of our approach by applying it to multiple distinct domains. Bruckner, S. Gröller, M.E. Schmidt, J. cluster color hierarchy VAST data visualization image color analysis image segmentation shape analysis visual analytics IEEE Transactions on Visualization and Computer Graphics comparative visualization focus+context visualization image set comparison 2013 vast13--219 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Vis4Heritage: Visual Analytics Approach on Grotto Wall Painting Degradations. For preserving the grotto wall paintings and protecting these historic cultural icons from the damage and deterioration in nature environment, a visual analytics framework and a set of tools are proposed for the discovery of degradation patterns. In comparison with the traditional analysis methods that used restricted scales, our method provides users with multi-scale analytic support to study the problems on site, cave, wall and particular degradation area scales, through the application of multidimensional visualization techniques. Several case studies have been carried out using real-world wall painting data collected from a renowned World Heritage site, to verify the usability and effectiveness of the proposed method. User studies and expert reviews were also conducted through by domain experts ranging from scientists such as microenvironment researchers, archivists, geologists, chemists, to practitioners such as conservators, restorers and curators. Kang, K. Liu, D. Yanli, E. Yuan, Y. Zhang, J. usability visual analytics VAST correlation cultural differences data visualization painting visual analytics IEEE Transactions on Visualization and Computer Graphics cultural heritage degradation visual analytics wall paintings 2013 vast13--220 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Higher-Order Conjunctive Relationships in Multidimensional Data Using a Hypergraph Query System. Visual exploration and analysis of multidimensional data becomes increasingly difficult with increasing dimensionality. We want to understand the relationships between dimensions of data, but lack flexible techniques for exploration beyond low-order relationships. Current visual techniques for multidimensional data analysis focus on binary conjunctive relationships between dimensions. Recent techniques, such as cross-filtering on an attribute relationship graph, facilitate the exploration of some higher-order conjunctive relationships, but require a great deal of care and precision to do so effectively. This paper provides a detailed analysis of the expressive power of existing visual querying systems and describes a more flexible approach in which users can explore n-ary conjunctive inter- and intra- dimensional relationships by interactively constructing queries as visual hypergraphs. In a hypergraph query, nodes represent subsets of values and hyperedges represent conjunctive relationships. Analysts can dynamically build and modify the query using sequences of simple interactions. The hypergraph serves not only as a query specification, but also as a compact visual representation of the interactive state. Using examples from several domains, focusing on the digital humanities, we describe the design considerations for developing the querying system and incorporating it into visual analysis tools. We analyze query expressiveness with regard to the kinds of questions it can and cannot pose, and describe how it simultaneously expands the expressiveness of and is complemented by cross-filtering. Shadoan, R. Weaver, C. graph VAST data analysis data visualization database languages marine vehicles semantics visual analytics IEEE Transactions on Visualization and Computer Graphics attribute relationship graphs digital humanities graph query language graph search higher-order conjunctive queries multidimensional data multivariate data analysis visual query language 2013 vast13--221 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Topic Competition on Social Media. How do various topics compete for public attention when they are spreading on social media? What roles do opinion leaders play in the rise and fall of competitiveness of various topics? In this study, we propose an expanded topic competition model to characterize the competition for public attention on multiple topics promoted by various opinion leaders on social media. To allow an intuitive understanding of the estimated measures, we present a timeline visualization through a metaphoric interpretation of the results. The visual design features both topical and social aspects of the information diffusion process by compositing ThemeRiver with storyline style visualization. ThemeRiver shows the increase and decrease of competitiveness of each topic. Opinion leaders are drawn as threads that converge or diverge with regard to their roles in influencing the public agenda change over time. To validate the effectiveness of the visual analysis techniques, we report the insights gained on two collections of Tweets: the 2012 United States presidential election and the Occupy Wall Street movement. Liu, S. Peng, T. Qu, H. Wei, E. Wu, Y. Xu, P. Zhu, J.J.H. social VAST data visualization mathematical model recruitment social network services visual analytics IEEE Transactions on Visualization and Computer Graphics agenda-setting information diffusion information propagation social media visuaization topic competition 2013 vast13--222 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Model Selection in Time Series Analysis. Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles. Aigner, W. Bogl, M. Filzmoser, P. Lammarsch, T. Miksch, S. Rind, A. statistics time series visual analytics VAST analytical models autoregressive processes data models mathematical model time series analysis IEEE Transactions on Visualization and Computer Graphics coordinated & multiple views model selection time series analysis visual analytics visual interaction 2013 vast13--223 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Multimodal Social Network Analysis: A Design Study with Social Scientists. Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing coauthorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an openended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA. Elmqvist, N. Ghani, S. Kwon, B.C. Lee, S. Yi, J.S. design study evaluation metrics network social visual analytics VAST complexity theory data visualization design methodology social network services user centered design visual analytics IEEE Transactions on Visualization and Computer Graphics design study interaction multimodal graphs node-link diagrams qualitative evaluation user-centered design 2013 vast13--224 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration. We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data. Bak, P. Nikkila, M. Packer, E. Polishchuk, V. Ship, H.J. cluster clustering interaction visual analytics VAST clustering algorithms data visualization heuristic algorithms image color analysis noise measurement shape analysis visual analytics IEEE Transactions on Visualization and Computer Graphics heuristic-based spatial clustering iinteractive visual clustering k-order a-(alpha)-shapes 2013 vast13--226 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips. As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them. Ferreira, N. Freire, J. Poco, J. Silva, C.T. Vo, H.T. insight VAST analytical models cities and towns data models data visualization mathematical model time factors visual analytics IEEE Transactions on Visualization and Computer Graphics nyc taxis spatio-temporal queries urban data visual exploration 2013 vast13--228 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Visual Traffic Jam Analysis Based on Trajectory Data. In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system. Lu, M. Wang, Z. Yuan, X. Zhang, J. van de Wetering, H. multiple views network VAST cities and towns data mining data visualization global positioning system road traffic traffic control trajectory urban areas IEEE Transactions on Visualization and Computer Graphics traffic jam propagation traffic visualization 2013 vast13--227 10/16/2013 IEEE Transactions on Visualization and Computer Graphics Using Interactive Visual Reasoning to Support Sense-Making: Implications for Design. This research aims to develop design guidelines for systems that support investigators and analysts in the exploration and assembly of evidence and inferences. We focus here on the problem of identifying candidate ŰinfluencersÜ within a community of practice. To better understand this problem and its related cognitive and interaction needs, we conducted a user study using a system called INVISQUE (INteractive Visual Search and QUery Environment) loaded with content from the ACM Digital Library. INVISQUE supports search and manipulation of results over a freeform infinite ŰcanvasÜ. The study focuses on the representations user create and their reasoning process. It also draws on some pre-established theories and frameworks related to sense-making and cognitive work in general, which we apply as a Űtheoretical lensesÜ to consider findings and articulate solutions. Analysing the user-study data in the light of these provides some understanding of how the high-level problem of identifying key players within a domain can translate into lower-level questions and interactions. This, in turn, has informed our understanding of representation and functionality needs at a level of description which abstracts away from the specifics of the problem at hand to the class of problems of interest. We consider the study outcomes from the perspective of implications for design. Attfield, S. Choudhury, S. Kodagoda, N. Rooney, C. Wong, B.L.W. interaction user study VAST analysis dataframe mode evaluation interaction interface design reasoning sense-making visual analytics IEEE Transactions on Visualization and Computer Graphics 2013 vast14--2346922 11/12/2014 IEEE Transactions on Visualization and Computer Graphics &#x0023;FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media. We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model. Cao, N. Collins, C. Lin, Y. Song, Y. Wen, Z. Zhao, J. machine learning social VAST data visualization feature extraction instruction sets media message systems social network services twitter visual analytics IEEE Transactions on Visualization and Computer Graphics anomaly detection information visualization machine learning retweeting threads social media visual analytics 2014 vast14--2346665 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Five-Level Design Framework for Bicluster Visualizations. Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools. North, C. Ramakrishnan, N. Sun, M. sensemaking visual analytics VAST bioinformatics cluster approximation data mining navigation visual analytics IEEE Transactions on Visualization and Computer Graphics biclusters coordinated relationships design framework interactive visual analytics 2014 vast14--2346752 11/12/2014 IEEE Transactions on Visualization and Computer Graphics ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery. Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms. Lex, A. Partl, C. Pfister, H. Schmalstieg, D. Streit, M. Strobelt, H. Wassermann, A.-M. interaction visual analytics VAST biomedical informatics data visualization drugs large-scale systems proteins visual analytics IEEE Transactions on Visualization and Computer Graphics drug discovery multi-relational data visual data analysis 2014 vast14--2346626 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Cupid: Cluster-Based Exploration of Geometry Generators with Parallel Coordinates and Radial Trees. Geometry generators are commonly used in video games and evaluation systems for computer vision to create geometric shapes such as terrains, vegetation or airplanes. The parameters of the generator are often sampled automatically which can lead to many similar or unwanted geometric shapes. In this paper, we propose a novel visual exploration approach that combines the abstract parameter space of the geometry generator with the resulting 3D shapes in a composite visualization. Similar geometric shapes are first grouped using hierarchical clustering and then nested within an illustrative parallel coordinates visualization. This helps the user to study the sensitivity of the generator with respect to its parameter space and to identify invalid parameter settings. Starting from a compact overview representation, the user can iteratively drill-down into local shape differences by clicking on the respective clusters. Additionally, a linked radial tree gives an overview of the cluster hierarchy and enables the user to manually split or merge clusters. We evaluate our approach by exploring the parameter space of a cup generator and provide feedback from domain experts. Beham, M. Gröller, M.E. Herzner, W. Kehrer, J. cluster clustering evaluation hierarchy overview parallel coordinates radial VAST computer vision data visualization games shape analysis three-dimensional displays video games IEEE Transactions on Visualization and Computer Graphics 3d shape analysis composite visualization hierarchical clustering illustrative parallel coordinates radial trees 2014 vast14--2346682 11/12/2014 IEEE Transactions on Visualization and Computer Graphics DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data. Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (e.g., with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (e.g., thousands of event types). DecisionFlow combines a scalable and dynamic temporal event data structure with interactive multi-view visualizations and ad hoc statistical analytics. We provide a detailed review of our methods, and present the results from a 12-person user study. The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events. Gotz, D. Stavropoulos, H. financial social user study visual analytics VAST aggregates data structures data visualization event detection medical diagnostic imaging sequential analysis IEEE Transactions on Visualization and Computer Graphics flow diagrams information visualization medical informatics temporal event sequences visual analytics 2014 vast14--2346747 11/12/2014 IEEE Transactions on Visualization and Computer Graphics DIA2: Web-based Cyberinfrastructure for Visual Analysis of Funding Portfolios. We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders. Chen, X. Dong, Z. Elmqvist, N. Johri, A. Madhavan, K. Vorvoreanu, M. Wong, Y. Xian, H. design study evaluation visual analytics VAST data visualization government research and development science - general visual analytics websites IEEE Transactions on Visualization and Computer Graphics casual visualization design study portfolio mining visual analytics web-based visualization 2014 vast14--2346919 11/12/2014 IEEE Transactions on Visualization and Computer Graphics EvoRiver: Visual Analysis of Topic Coopetition on Social Media. Cooperation and competition (jointly called ücoopetitioný) are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., ütopic leadersý) affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data). Liang, R. Liu, S. Peng, T. Sun, G. Wu, Y. Zhu, J.J.H. business social visual analytics VAST cooperation data visualization media social network services visual analytics IEEE Transactions on Visualization and Computer Graphics information diffusion information propagation time-based visualization topic coopetition 2014 vast14--2346575 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Finding Waldo: Learning about Users from their Interactions. Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems. Brown, E.T. Chang, R. Endert, A. Lin, Q. Ottley, A. Souvenir, R. Zhao, H. collaboration experiment interaction machine learning visual analytics VAST accuracy computers data visualization encoding mice visual analytics IEEE Transactions on Visualization and Computer Graphics analytic provenance applied machine learning user interactions visualization 2014 vast14--2346743 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Footprints: A Visual Search Tool that Supports Discovery and Coverage Tracking. Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity üdiscoverage,ý discovery of relevant material and tracking coverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. Coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback. Ahern, S. Bart, E. Damico, K. Isaacs, E. Singhal, M. document overview VAST histograms information filters iterative methods space exploration text analysis tracking IEEE Transactions on Visualization and Computer Graphics coverage tracking discoverage discovery search visualization document triage interactive histograms visual cues 2014 vast14--2346753 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks. Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness). Arrieta-Ortiz, M.L. Bonneau, R. Chen, X. Doraiswamy, H. Hafemeister, C. Madar, A. Miraldi, E. Silva, C.T. Yu, B. coordinated views network VAST bioinformatics biological system modeling browsers computational modeling data models data visualization gene expression genomics IEEE Transactions on Visualization and Computer Graphics gene regulatory network web-based visualization 2014 vast14--2346482 11/12/2014 IEEE Transactions on Visualization and Computer Graphics INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data. Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records. Bertini, E. Krause, J. Perer, A. case study visual analytics VAST algorithm design and analysis data models data visualization feature extraction prediction algorithms predictive models IEEE Transactions on Visualization and Computer Graphics classification feature selection high-dimensional data predictive modeling visual analytics 2014 vast14--2346591 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Interactive Visual Analysis of Image-Centric Cohort Study Data. Epidemiological population studies impose information about a set of subjects (a cohort) to characterize disease-specific risk factors. Cohort studies comprise heterogenous variables describing the medical condition as well as demographic and lifestyle factors and, more recently, medical image data. We propose an Interactive Visual Analysis (IVA) approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation. We incorporate image data, which involves shape-based object detection and the derivation of attributes describing the object shape. The concurrent investigation of image-based and non-image data is realized in a web-based multiple coordinated view system, comprising standard views from information visualization and epidemiological data representations such as pivot tables. The views are equipped with brushing facilities and augmented by 3D shape renderings of the segmented objects, e.g., each bar in a histogram is overlaid with a mean shape of the associated subgroup of the cohort. We integrate an overview visualization, clustering of variables and object shape for data-driven subgroup definition and statistical key figures for measuring the association between variables. We demonstrate the IVA approach by validating and generating hypotheses related to lower back pain as part of a qualitative evaluation. Hegenscheid, K. Klemm, P. Lawonn, K. Oeltze-Jafra, S. Preim, B. Volzke, H. brushing clustering evaluation overview VAST data visualization diseases image segmentation medical diagnostic imaging risk management shape analysis visual analytics IEEE Transactions on Visualization and Computer Graphics epidemiology interactive visual analysis spine 2014 vast14--2346481 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Knowledge Generation Model for Visual Analytics. Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on. Chul, B. Ellis, G. Keim, D.A. Sacha, D. Stoffel, A. Stoffel, F. visual analytics VAST analytical models computational modeling data models data visualization visual analytics IEEE Transactions on Visualization and Computer Graphics interaction knowledge generation reasoning visual analytics visualization taxonomies and models 2014 vast14--2346912 11/12/2014 IEEE Transactions on Visualization and Computer Graphics LoyalTracker: Visualizing Loyalty Dynamics in Search Engines. The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines. Liu, S. Qu, H. Shi, C. Wu, Y. Zhou, H. visual analytics VAST behavioral science data visualization information analysis search engines search methods visual analytics IEEE Transactions on Visualization and Computer Graphics log data visualization stacked graphs text visualization time-series visualization 2014 vast14--2346578 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations. An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used. Gratzl, S. Muhlbacher, T. Piringer, H. Sedlmair, M. Streit, M. VAST algorithm design and analysis approximation algorithms complexity theory context data visualization software algorithms visualization IEEE Transactions on Visualization and Computer Graphics integration interactive algorithms problem subdivision user involvement visual analytics infrastructures 2014 vast14--2346920 11/12/2014 IEEE Transactions on Visualization and Computer Graphics OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media. It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow. Liu, M. Liu, S. Wu, F. Wu, Y. Yan, K. business graph social theory usability VAST data visualization information analysis media social network services twitter visual analytics IEEE Transactions on Visualization and Computer Graphics influence estimation kernel density estimation level-of-detail opinion diffusion opinion flow opinion visualization 2014 vast14--2346926 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement. In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets. Ebert, D.S. Maciejewski, R. Malik, A. McCullough, S. Towers, S. geospatial visual analytics VAST decision making forecasting geospatial analysis market research spatiotemporal phenomena time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics law enforcement natural scales seasonal trend decomposition based on loess (stl) visual analytics 2014 vast14--2346574 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics. As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records. Gotz, D. Perer, A. Stolper, C.D. evaluation visual analytics VAST algorithm design and analysis data visualization heuristic algorithms unsolicited electronic mail visual analytics IEEE Transactions on Visualization and Computer Graphics electronic medical records information visualization interactive machine learning progressive visual analytics 2014 vast14--2346930 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Run Watchers: Automatic Simulation-Based Decision Support in Flood Management. In this paper, we introduce a simulation-based approach to design protection plans for flood events. Existing solutions require a lot of computation time for an exhaustive search, or demand for a time-consuming expert supervision and steering. We present a faster alternative based on the automated control of multiple parallel simulation runs. Run Watchers are dedicated system components authorized to monitor simulation runs, terminate them, and start new runs originating from existing ones according to domain-specific rules. This approach allows for a more efficient traversal of the search space and overall performance improvements due to a re-use of simulated states and early termination of failed runs. In the course of search, Run Watchers generate large and complex decision trees. We visualize the entire set of decisions made by Run Watchers using interactive, clustered timelines. In addition, we present visualizations to explain the resulting response plans. Run Watchers automatically generate storyboards to convey plan details and to justify the underlying decisions, including those which leave particular buildings unprotected. We evaluate our solution with domain experts. Cornel, D. Gröller, M.E. Horvath, Z. Konev, A. Perdigao, R.A.P. Sadransky, B. Waser, J. VAST buildings computational modeling data visualization decision making decision trees monitoring visual analytics IEEE Transactions on Visualization and Computer Graphics decision making disaster management simulation control storytelling visual evidence 2014 vast14--2346573 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Supporting Communication and Coordination in Collaborative Sensemaking. When people work together to analyze a data set, they need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate activities amongst team members. Sharing externalizations (recorded information such as notes) could increase awareness and assist with team communication and coordination. However, we currently know little about how to provide tool support for this sort of sharing. We explore how linked common work (LCW) can be employed within a `collaborative thinking space', to facilitate synchronous collaborative sensemaking activities in Visual Analytics (VA). Collaborative thinking spaces provide an environment for analysts to record, organize, share and connect externalizations. Our tool, CLIP, extends earlier thinking spaces by integrating LCW features that reveal relationships between collaborators' findings. We conducted a user study comparing CLIP to a baseline version without LCW. Results demonstrated that LCW significantly improved analytic outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. LCW enabled them to maintain awareness of each other's activities and findings and link those findings to their own work, preventing disruptive oral awareness notifications. Mahyar, N. Tory, M. awareness sensemaking user study visual analytics VAST data visualization image resolution measurement optimization quality assessment rendering (computer graphics) video recording IEEE Transactions on Visualization and Computer Graphics collaboration collaborative thinking space externalization linked common work sensemaking 2014 vast14--2346754 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Spinel Explorer&#x2014;Interactive Visual Analysis of Spinel Group Minerals. Geologists usually deal with rocks that are up to several thousand million years old. They try to reconstruct the tectonic settings where these rocks were formed and the history of events that affected them through the geological time. The spinel group minerals provide useful information regarding the geological environment in which the host rocks were formed. They constitute excellent indicators of geological environments (tectonic settings) and are of invaluable help in the search for mineral deposits of economic interest. The current workflow requires the scientists to work with different applications to analyze spine data. They do use specific diagrams, but these are usually not interactive. The current workflow hinders domain experts to fully exploit the potentials of tediously and expensively collected data. In this paper, we introduce the Spinel Explorer-an interactive visual analysis application for spinel group minerals. The design of the Spinel Explorer and of the newly introduced interactions is a result of a careful study of geologists' tasks. The Spinel Explorer includes most of the diagrams commonly used for analyzing spinel group minerals, including 2D binary plots, ternary plots, and 3D Spinel prism plots. Besides specific plots, conventional information visualization views are also integrated in the Spinel Explorer. All views are interactive and linked. The Spinel Explorer supports conventional statistics commonly used in spinel minerals exploration. The statistics views and different data derivation techniques are fully integrated in the system. Besides the Spinel Explorer as newly proposed interactive exploration system, we also describe the identified analysis tasks, and propose a new workflow. We evaluate the Spinel Explorer using real-life data from two locations in Argentina: the Frontal Cordillera in Central Andes and Patagonia. We describe the new findings of the geologists which would have been much more difficult to achieve using the cur- ent workflow only. Very positive feedback from geologists confirms the usefulness of the Spinel Explorer. Bjerg, E. Castro, S.M. Ferracutti, G. Gargiulo, M.F. Gröller, M.E. Lujan Ganuza, M. Matkovic, K. history statistics VAST data visualization geology minerals rocks three-dimensional displays IEEE Transactions on Visualization and Computer Graphics and environmental sciences coordinated and multiple views design studies interactive visual analysis space visualization in earth 2014 vast14--2346572 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Transforming Scagnostics to Reveal Hidden Features. Scagnostics (Scatterplot Diagnostics) were developed by Wilkinson et al. based on an idea of Paul and John Tukey, in order to discern meaningful patterns in large collections of scatterplots. The Tukeys' original idea was intended to overcome the impediments involved in examining large scatterplot matrices (multiplicity of plots and lack of detail). Wilkinson's implementation enabled for the first time scagnostics computations on many points as well as many plots. Unfortunately, scagnostics are sensitive to scale transformations. We illustrate the extent of this sensitivity and show how it is possible to pair statistical transformations with scagnostics to enable discovery of hidden structures in data that are not discernible in untransformed visualizations. Nhon, T. Wilkinson, L. scatterplot VAST data visualization feature extraction shape analysis visual analytics IEEE Transactions on Visualization and Computer Graphics high-dimensional visual analytics scagnostics scatterplot matrix transformation 2014 vast14--2346913 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VAET: A Visual Analytics Approach for E-Transactions Time-Series. Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET. Barlowe, S. Chen, W. Hu, Y. Huang, X. Xie, C. Yang, J. case study user study visual analytics VAST data visualization decision trees feature extraction probabilistic logic time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics e-transaction time-series visual analytics 2014 vast14--2346677 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VarifocalReader &#x2014; In-Depth Visual Analysis of Large Text Documents. Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies. Ertl, T. John, M. Koch, S. Muller, A. Worner, M. document machine learning navigation overview text VAST data mining data visualization document handling interactive systems natural language processing navigation tag clouds text mining IEEE Transactions on Visualization and Computer Graphics distant reading document analysis literary analysis machine learning natural language processing text mining visual analytics 2014 vast14--2346911 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure. We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain. Abram, G. Afzal, S. Ebert, D.S. Elmqvist, N. Gaither, K. Kennedy, S. Kne, L. Ko, S. Ribarsky, W. Tolone, W. Van Riper, D. Wang, X. Xia, J. Zhao, J. visual analytics VAST analytical models computational modeling data models emergency serivces meteorology supply chains visual analytics IEEE Transactions on Visualization and Computer Graphics computational steering critical infrastructure homeland security visual analytics 2014 vast14--2346594 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Abstraction and Exploration of Multi-class Scatterplots. Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach. Chen, H. Chen, W. Gu, W. Liu, Z. Ma, K.-L. Mei, H. Zhou, K. scatterplot VAST data visualization estimation image color analysis market research noise statistical analysis visualization IEEE Transactions on Visualization and Computer Graphics overdraw reduction sampling scatterplot visual abstraction 2014 vast14--2346898 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of Public Utility Service Problems in a Metropolis. Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions. Chen, J. Ma, J. Sun, L. Xu, B. Yanli, E. Yuan, X. Zhang, J. Zhao, Y. interaction visual analytics VAST cities and towns color analysis distribution functions graphical models urban areas visual analytics IEEE Transactions on Visualization and Computer Graphics aggregate evidence-based decision making utility services visual analytics 2014 vast14--2346751 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles. Researchers assess the quality of an ocean model by comparing its output to that of a previous model version or to observations. One objective of the comparison is to detect and to analyze differences and similarities between both data sets regarding geophysical processes, such as particular ocean currents. This task involves the analysis of thousands or hundreds of thousands of geographically referenced temporal profiles in the data. To cope with the amount of data, modelers combine aggregation of temporal profiles to single statistical values with visual comparison. Although this strategy is based on experience and a well-grounded body of expert knowledge, our discussions with domain experts have shown that it has two limitations: (1) using a single statistical measure results in a rather limited scope of the comparison and in significant loss of information, and (2) the decisions modelers have to make in the process may lead to important aspects being overlooked. Dobslaw, H. Dransch, D. Kothur, P. Sips, M. clustering visual analytics VAST analytical models clustering algorithms computational modeling data models geospatial analysis oceans visualization IEEE Transactions on Visualization and Computer Graphics cluster ensembles geospatial time model assessment ocean modeling series visual analytics visual comparison 2014 vast14--2346744 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles. In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a naiČve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the übestý points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system. Gracanin, D. Hauser, H. Jelovic, M. Matkovic, K. Purgathofer, W. Splechtna, R. Stehno, B. case study experiment visual analytics VAST analytical models computational modeling data models optimization simulation visualization IEEE Transactions on Visualization and Computer Graphics automatic optimization integrated design environment interactive visual analysis simulation visual steering 2014 vast14--2346746 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Exploration of Sparse Traffic Trajectory Data. In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system. Lu, M. Qu, H. Wang, Z. Wu, Q. Ye, T. Yuan, J. Yuan, X. animation graph VAST aircraft navigation data visualization trajectory visual analytics IEEE Transactions on Visualization and Computer Graphics dynamic graph visualization sparse traffic trajectory traffic congestion traffic visualization 2014 vast14--2346660 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Methods for Analyzing Probabilistic Classification Data. Multi-class classifiers often compute scores for the classification samples describing probabilities to belong to different classes. In order to improve the performance of such classifiers, machine learning experts need to analyze classification results for a large number of labeled samples to find possible reasons for incorrect classification. Confusion matrices are widely used for this purpose. However, they provide no information about classification scores and features computed for the samples. We propose a set of integrated visual methods for analyzing the performance of probabilistic classifiers. Our methods provide insight into different aspects of the classification results for a large number of samples. One visualization emphasizes at which probabilities these samples were classified and how these probabilities correlate with classification error in terms of false positives and false negatives. Another view emphasizes the features of these samples and ranks them by their separation power between selected true and false classifications. We demonstrate the insight gained using our technique in a benchmarking classification dataset, and show how it enables improving classification performance by interactively defining and evaluating post-classification rules. Alsallakh, B. Hanbury, A. Hauser, H. Miksch, S. Rauber, A. insight machine learning VAST data visualization electric breakdown histograms image color analysis probabilistic logic probability IEEE Transactions on Visualization and Computer Graphics confusion analysis feature evaluation and selection probabilistic classification visual inspection 2014 vast14--2346755 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling. Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science. Bertini, E. Cook, R. Dasgupta, A. Hargrove, W. Huntzinger, D.N. Poco, J. Schwalm, C.R. Silva, C.T. Wei, Y. clustering interaction visual analytics VAST adaptation models analytical models computational modeling data models meteorology visual analytics IEEE Transactions on Visualization and Computer Graphics climate model clustering matrix optimization similarity 2014 vast14--2346893 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visualizing Mobility of Public Transportation System. Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers. Arisona, S.M. Erath, A. Fu, C. Qu, H. Zeng, W. network VAST cities and towns data visualization radiofrequency identification schedules transportation urban areas visual analytics IEEE Transactions on Visualization and Computer Graphics mobility public transportation visual analytics 2014 vast14--7042491 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) YMCA &#x2014; Your mesh comparison application. Polygonal meshes can be created in several different ways. In this paper we focus on the reconstruction of meshes from point clouds, which are sets of points in 3D. Several algorithms that tackle this task already exist, but they have different benefits and drawbacks, which leads to a large number of possible reconstruction results (i.e., meshes). The evaluation of those techniques requires extensive comparisons between different meshes which is up to now done by either placing images of rendered meshes side-by-side, or by encoding differences by heat maps. A major drawback of both approaches is that they do not scale well with the number of meshes. This paper introduces a new comparative visual analysis technique for 3D meshes which enables the simultaneous comparison of several meshes and allows for the interactive exploration of their differences. Our approach gives an overview of the differences of the input meshes in a 2D view. By selecting certain areas of interest, the user can switch to a 3D representation and explore the spatial differences in detail. To inspect local variations, we provide a magic lens tool in 3D. The location and size of the lens provide further information on the variations of the reconstructions in the selected area. With our comparative visualization approach, differences between several mesh reconstruction algorithms can be easily localized and inspected. Auzinger, T. Bruckner, S. Gröller, M.E. Preiner, R. Schmidt, J. Wimmer, M. evaluation overview VAST context kernel lenses reconstruction algorithms surface reconstruction three-dimensional displays 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) 3d data exploration comparative visualization focus+context mesh comparison visual analysis 2014 vast14--7042483 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Weaving a carpet from log entries: A network security visualization built with co-creation. We created a pixel map for multivariate data based on an analysis of the needs of network security engineers. Parameters of a log record are shown as pixels and these pixels are stacked to represent a record. This allows a broad view of a data set on one screen while staying very close to the raw data and to expose common and rare patterns of user behavior through the visualization itself (the "Carpet"). Visualizations that immediately point to areas of suspicious activity without requiring extensive filtering, help network engineers investigating unknown computer security incidents. Most of them, however, have limited knowledge of advanced visualization techniques, while many designers and data scientists are unfamiliar with computer security topics. To bridge this gap, we developed visualizations together with engineers, following a co-creative process. We will show how we explored the scope of the engineers' tasks and how we jointly developed ideas and designs. Our expert evaluation indicates that this visualization helps to scan large parts of log files quickly and to define areas of interest for closer inspection. Dork, M. Herrmann, I. Landstorfer, J. Stange, J. Wettach, R. evaluation network pixel security VAST visual analytics 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) multidimensional data network security and intrusion pixel-oriented techniques task and requirements analysis 2014 vast14--7042497 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual exploratory tool for storyline generation. Storyline visualizations are useful to let people explore works of literature. It was first introduced in XKCD's Movie Narrative Charts as a hand-drawn illustration [1]. Tanahashi and Ma proposed some considerations on the design of the storyline visualization and automation became meaningful because of its aesthetic and clear representation style [2]. Shixia Liu and Yingcai Wu's StoryFlow had become the next step in storyline visualization [3]. With the efficient optimization approach on automation, representation of the complex stories became efficient and enabled users to track and understand the story easier. In all cases before generating the visualizations, an input file must be created manually. In this work we propose a semi-automatic exploratory visual tool for preparation of such input files. Akyigit, E.E. Balcisoy, S. Cengiz, T. Yildirim, O.B. VAST automation context databases educational institutions motion pictures tag clouds visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) 2014 vast14--7042485 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analysis of patterns in multiple amino acid mutation graphs. Proteins are essential parts in all living organisms. They consist of sequences of amino acids. An interaction with reactive agent can stimulate a mutation at a specific position in the sequence. This mutation may set off a chain reaction, which effects other amino acids in the protein. Chain reactions need to be analyzed, as they may invoke unwanted side effects in drug treatment. A mutation chain is represented by a directed acyclic graph, where amino acids are connected by their mutation dependencies. As each amino acid may mutate individually, many mutation graphs exist. To determine important impacts of mutations, experts need to analyze and compare common patterns in these mutations graphs. Experts, however, lack suitable tools for this purpose. We present a new system for the search and the exploration of frequent patterns (i.e., motifs) in mutation graphs. We present a fast pattern search algorithm specifically developed for finding biologically relevant patterns in many mutation graphs (i.e., many labeled acyclic directed graphs). Our visualization system allows an interactive exploration and comparison of the found patterns. It enables locating the found patterns in the mutation graphs and in the 3D protein structures. In this way, potentially interesting patterns can be discovered. These patterns serve as starting point for a further biological analysis. In cooperation with biologists, we use our approach for analyzing a real world data set based on multiple HIV protease sequences. Bremm, S. Hamacher, K. Keul, F. Lenz, O. von Landesberger, T. graph interaction VAST algorithm design and analysis amino acids drugs proteins three-dimensional displays visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) biologic visualization biology graph visualization motif search motif visualization mutations pattern visualization 2014 vast14--7042489 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Vismate: Interactive visual analysis of station-based observation data on climate changes. We present a new approach to visualizing the climate data of multi-dimensional, time-series, and geo-related characteristics. Our approach integrates three new highly interrelated visualization techniques, and uses the same input data types as in the traditional model-based analysis methods. As the main visualization view, Global Radial Map is used to identify the overall state of climate changes and provide users with a compact and intuitive view for analyzing spatial and temporal patterns at the same time. Other two visualization techniques, providing complementary views, are specialized in analysing time trend and detecting abnormal cases, which are two important analysis tasks in any climate change study. Case studies and expert reviews have been conducted, through which the effectiveness and scalability of the proposed approach has been confirmed. Li, J. Meng, Z. Zhang, K. radial VAST data visualization image color analysis layout meteorology spatiotemporal phenomena time series analysis visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) climate changes radial layout spatiotemporal visualization station-based observation data visual analytics 2014 vast14--7042487 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Using visualizations to monitor changes and harvest insights from a global-scale logging infrastructure at Twitter. Logging user activities is essential to data analysis for internet products and services. Twitter has built a unified logging infrastructure that captures user activities across all clients it owns, making it one of the largest datasets in the organization. This paper describes challenges and opportunities in applying information visualization to log analysis at this massive scale, and shows how various visualization techniques can be adapted to help data scientists extract insights. In particular, we focus on two scenarios: (1) monitoring and exploring a large collection of log events, and (2) performing visual funnel analysis on log data with tens of thousands of event types. Two interactive visualizations were developed for these purposes: we discuss design choices and the implementation of these systems, along with case studies of how they are being used in day-to-day operations at Twitter. Lin, J. Wongsuphasawat, K. VAST data analysis data visualization monitoring radar twitter vegetation visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) funnel analysis information visualization log analysis log visualization session analysis visual analytics 2014 vast14--7042476 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Towards interactive, intelligent, and integrated multimedia analytics. The size and importance of visual multimedia collections grew rapidly over the last years, creating a need for sophisticated multimedia analytics systems enabling large-scale, interactive, and insightful analysis. These systems need to integrate the human's natural expertise in analyzing multimedia with the machine's ability to process large-scale data. The paper starts off with a comprehensive overview of representation, learning, and interaction techniques from both the human's and the machine's point of view. To this end, hundreds of references from the related disciplines (visual analytics, information visualization, computer vision, multimedia information retrieval) have been surveyed. Based on the survey, a novel general multimedia analytics model is synthesized. In the model, the need for semantic navigation of the collection is emphasized and multimedia analytics tasks are placed on the exploration-search axis. The axis is composed of both exploration and search in a certain proportion which changes as the analyst progresses towards insight. Categorization is proposed as a suitable umbrella task realizing the exploration-search axis in the model. Finally, the pragmatic gap, defined as the difference between the tight machine categorization model and the flexible human categorization model is identified as a crucial multimedia analytics topic. Worring, M. Zahalka, J. insight interaction navigation overview visual analytics VAST browsers data visualization feature extraction multimedia communication semantics streaming media visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) machine learning multimedia (image/video/music) visualization 2014 vast14--7042494 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) TopicPanorama: A full picture of relevant topics. We present a visual analytics approach to developing a full picture of relevant topics discussed in multiple sources such as news, blogs, or micro-blogs. The full picture consists of a number of common topics among multiple sources as well as distinctive topics. The key idea behind our approach is to jointly match the topics extracted from each source together in order to interactively and effectively analyze common and distinctive topics. We start by modeling each textual corpus as a topic graph. These graphs are then matched together with a consistent graph matching method. Next, we develop an LOD-based visualization for better understanding and analysis of the matched graph. The major feature of this visualization is that it combines a radially stacked tree visualization with a density-based graph visualization to facilitate the examination of the matched topic graph from multiple perspectives. To compensate for the deficiency of the graph matching algorithm and meet different users' needs, we allow users to interactively modify the graph matching result. We have applied our approach to various data including news, tweets, and blog data. Qualitative evaluation and a real-world case study with domain experts demonstrate the promise of our approach, especially in support of analyzing a topic-graph-based full picture at different levels of detail. Chen, J. Guo, B. Liu, S. Wang, X. Zhu, J.J.H. case study evaluation graph visual analytics VAST algorithm design and analysis correlation google government internet layout visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) graph matching graph visualization level-of-detail topic graph user interactions 2014 vast14--7042498 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) TimeGraph: A data management framework for visual analytics of large multivariate time-oriented networks. Large multivariate time-oriented networks have been gaining an increasing relevance in different domains. In order to support Visual Analytics processes on this kind of data, appropriate storage and retrieval methods are needed that take into account the scale, dimensionality, and in particular the complex nature of time. We introduce TimeGraph, a data management framework consisting of a data model and two levels of abstraction. TimeGraph captures both the topology of networks and the inherent structure of time into a property graph data structure, and transparently handles them by graph-based operations. TimeGraph aims to be an expressive, easy-to-use and extensible framework, enabling data reduction by selection and aggregation over both the temporal and the topological properties of data, to foster interactive visualization and analysis. Amor-Amoros, A. Federico, P. Miksch, S. graph visual analytics VAST calendars complexity theory data models data visualization databases educational institutions visual analytics 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) 2014 vast14--7042493 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Serendip: Topic model-driven visual exploration of text corpora. Exploration and discovery in a large text corpus requires investigation at multiple levels of abstraction, from a zoomed-out view of the entire corpus down to close-ups of individual passages and words. At each of these levels, there is a wealth of information that can inform inquiry ô from statistical models, to metadata, to the researcher's own knowledge and expertise. Joining all this information together can be a challenge, and there are issues of scale to be combatted along the way. In this paper, we describe an approach to text analysis that addresses these challenges of scale and multiple information sources, using probabilistic topic models to structure exploration through multiple levels of inquiry in a way that fosters serendipitous discovery. In implementing this approach into a tool called Serendip, we incorporate topic model data and metadata into a highly reorderable matrix to expose corpus level trends; extend encodings of tagged text to illustrate probabilistic information at a passage level; and introduce a technique for visualizing individual word rankings, along with interaction techniques and new statistical methods to create links between different levels and information types. We describe example uses from both the humanities and visualization research that illustrate the benefits of our approach. Alexander, E. Gleicher, M. Kohlmann, J. Valenza, R. Witmore, M. interaction matrix text VAST adaptation models data models data visualization market research measurement probabilistic logic vectors 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) text visualization topic modeling 2014 vast14--7042496 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) PEARL: An interactive visual analytic tool for understanding personal emotion style derived from social media. Hundreds of millions of people leave digital footprints on social media (e.g., Twitter and Facebook). Such data not only disclose a person's demographics and opinions, but also reveal one's emotional style. Emotional style captures a person's patterns of emotions over time, including his overall emotional volatility and resilience. Understanding one's emotional style can provide great benefits for both individuals and businesses alike, including the support of self-reflection and delivery of individualized customer care. We present PEARL, a timeline-based visual analytic tool that allows users to interactively discover and examine a person's emotional style derived from this person's social media text. Compared to other visual text analytic systems, our work offers three unique contributions. First, it supports multi-dimensional emotion analysis from social media text to automatically detect a person's expressed emotions at different time points and summarize those emotions to reveal the person's emotional style. Second, it effectively visualizes complex, multi-dimensional emotion analysis results to create a visual emotional profile of an individual, which helps users browse and interpret one's emotional style. Third, it supports rich visual interactions that allow users to interactively explore and validate emotion analysis results. We have evaluated our work extensively through a series of studies. The results demonstrate the effectiveness of our tool both in emotion analysis from social media and in support of interactive visualization of the emotion analysis results. Gou, L. Wang, F. Zhao, J. Zhou, M.X. social text VAST analytical models computational modeling engines media mood resilience visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) affective and mood modeling information visualization personal emotion analytics social media text twitter 2014 vast14--7042492 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Multi-model semantic interaction for text analytics. Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection. Bradel, L. House, L. Leman, S. North, C. document interaction text VAST analytical models data models data visualization layout pipelines semantics visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) semantic interaction sensemaking text analytics visual analytics 2014 vast14--7042495 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Integrating predictive analytics and social media. A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success. Ertl, T. Koch, S. Kruger, R. Lu, Y. Maciejewski, R. Thom, D. Wang, F. social user study VAST analytical models correlation media motion pictures predictive models twitter youtube 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) feature selection predictive analytics social media 2014 vast14--7042488 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) HydroQual: Visual analysis of river water quality. Economic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40], River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56], Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotemporal data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts. Accorsi, P. Braud, A. Bringay, S. Cernesson, F. Fabregue, M. Lalande, N. Le Ber, F. Poncelet, P. Sallaberry, A. Teisseire, M. case study data mining evaluation VAST biology data mining data visualization databases rivers water pollution water resources 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) spatiotemporal data mining and visualization visual analytics water quality 2014 vast14--7042480 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Feedback-driven interactive exploration of large multidimensional data supported by visual classifier. The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems. Behrisch, M. Korkmaz, F. Schreck, T. Shao, L. case study focus+context high-dimensional data metrics zoom VAST data models data visualization decision support systems decision trees space exploration vectors visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) interesting view problem relevance feedback user preference model view space exploration framework 2014 vast14--7042477 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Feature-driven visual analytics of soccer data. Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach. Deussen, O. Janetzko, H. Keim, D.A. Sacha, D. Schreck, T. Stein, M. visual analytics VAST data mining data visualization feature extraction games trajectory visual analytics 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) soccer analysis sport analytics visual analytics 2014 vast14--7042490 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) BoundarySeer: Visual analysis of 2D boundary changes. Boundary changes exist ubiquitously in our daily life. From the Antarctic ozone hole to the land desertification, and from the territory of a country to the area within one-hour reach from a downtown location, boundaries change over time. With a large number of time-varying boundaries recorded, people often need to analyze the changes, detect their similarities or differences, and find out spatial and temporal patterns of the evolution for various applications. In this paper, we present a comprehensive visual analytics system, BoundarySeer, to help users gain insight into the changes of boundaries. Our system consists of four major viewers: 1) a global viewer to show boundary groups based on their similarity and the distribution of boundary attributes such as smoothness and perimeter; 2) a region viewer to display the regions encircled by the boundaries and how they are affected by boundary changes; 3) a trend viewer to reveal the temporal patterns in the boundary evolution and potential spatio-temporal correlations; 4) a directional change viewer to encode movements of boundary segments in different directions. Quantitative analyses of boundaries (e.g., similarity measurement and adaptive clustering) and intuitive visualizations (e.g., density map and ThemeRiver) are integrated into these viewers, which enable users to explore boundary changes from different aspects and at different scales. Case studies with two real-world datasets have been carried out to demonstrate the effectiveness of our system. Chen, W. Gröller, M.E. Ni, L.M. Qu, H. Wu, W. Zheng, Y. clustering insight visual analytics VAST data visualization encoding heating market research power system stability stability analysis visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) boundary change contour map radial visualization scatter plot themeriver visual analytics 2014 vast14--7042478 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Baseball4D: A tool for baseball game reconstruction & visualization. While many sports use statistics and video to analyze and improve game play, baseball has led the charge throughout its history. With the advent of new technologies that allow all players and the ball to be tracked across the entire field, it is now possible to bring this understanding to another level. From discrete positions across time, we present techniques to reconstruct entire baseball games and visually explore each play. This provides opportunities to not only derive new metrics for the game, but also allow us to investigate existing measures with targeted visualizations. In addition, our techniques allow users to filter on demand so specific situations can be analyzed both in general and according to those situations. We show that gameplay can be accurately reconstructed from the raw position data and discuss how visualization and statistical methods can combine to better inform baseball analyses. Dietrich, C. Koop, D. Silva, C.T. Vo, H.T. filter history metrics statistics VAST data visualization games heating measurement sports equipment trajectory visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) baseball baseball metrics event data game reconstruction sports analytics sports visualization 2014 vast14--7042484 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) Analyzing high-dimensional multivar&#x00ED;ate network links with integrated anomaly detection, highlighting and exploration. This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study. Afzal, S. Chae, J. Chen, M. Ebert, D.S. Jang, Y. Ko, S. Malik, A. Walton, S. Yang, Y. categorical network visual analytics VAST airports calendars data visualization delays educational institutions instruction sets visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) i36 [computer graphics]: methodology and techniques ô interaction techniques i38 [computer graphics]: applications ô visual analytics 2014 vast14--7042481 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) An integrated visual analysis system for fusing MR spectroscopy and multi-modal radiology imaging. For cancers such as glioblastoma multiforme, there is an increasing interest in defining "biological target volumes" (BTV), high tumour-burden regions which may be targeted with dose boosts in radiotherapy. The definition of a BTV requires insight into tumour characteristics going beyond conventionally defined radiological abnormalities and anatomical features. Molecular and biochemical imaging techniques, like positron emission tomography, the use of Magnetic Resonance (MR) Imaging contrast agents or MR Spectroscopy deliver this information and support BTV delineation. MR Spectroscopy Imaging (MRSI) is the only non-invasive technique in this list. Studies with MRSI have shown that voxels with certain metabolic signatures are more susceptible to predict the site of relapse. Nevertheless, the discovery of complex relationships between a high number of different metabolites, anatomical, molecular and functional features is an ongoing topic of research ô still lacking appropriate tools supporting a smooth workflow by providing data integration and fusion of MRSI data with other imaging modalities. We present a solution bridging this gap which gives fast and flexible access to all data at once. By integrating a customized visualization of the multi-modal and multi-variate image data with a highly flexible visual analytics (VA) framework, it is for the first time possible to interactively fuse, visualize and explore user defined metabolite relations derived from MRSI in combination with markers delivered by other imaging modalities. Real-world medical cases demonstrate the utility of our solution. By making MRSI data available both in a VA tool and in a multi-modal visualization renderer we can combine insights from each side to arrive at a superior BTV delineation. We also report feedback from domain experts indicating significant positive impact in how this work can improve the understanding of MRSI data and its integration into radiotherapy pl- nning. Buhler, K. Ken, S. Laprie, A. Matkovic, K. Nunes, M. Rowland, B. Schlachter, M. insight visual analytics VAST biomedical imaging brushes data visualization planning spectroscopy tumors 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) brain cancer medical decision support systems mr spectroscopy multi-modality data radiotherapy planning visualization 2014 vast14--7042482 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) An insight- and task-based methodology for evaluating spatiotemporal visual analytics. We present a method for evaluating visualizations using both tasks and exploration, and demonstrate this method in a study of spatiotemporal network designs for a visual analytics system. The method is well suited for studying visual analytics applications in which users perform both targeted data searches and analyses of broader patterns. In such applications, an effective visualization design is one that helps users complete tasks accurately and efficiently, and supports hypothesis generation during open-ended exploration. To evaluate both of these aims in a single study, we developed an approach called layered insight- and task-based evaluation (LITE) that interposes several prompts for observations about the data model between sequences of predefined search tasks. We demonstrate the evaluation method in a user study of four network visualizations for spatiotemporal data in a visual analytics application. Results include findings that might have been difficult to obtain in a single experiment using a different methodology. For example, with one dataset we studied, we found that on average participants were faster on search tasks using a force-directed layout than using our other designs; at the same time, participants found this design least helpful in understanding the data. Our contributions include a novel evaluation method that combines well-defined tasks with exploration and observation, an evaluation of network visualization designs for spatiotemporal visual analytics, and guidelines for using this evaluation method. Gomez, S.R. Guo, H. Laidlaw, D.H. Ziemkiewicz, C. evaluation experiment insight network user study visual analytics VAST accuracy data models data visualization layout spatiotemporal phenomena visual analytics 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) evaluation methodology information visualization insight-based evaluation network visualization visual analytics 2014 vast14--7042486 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) A visual reasoning approach for data-driven transport assessment on urban roads. Transport assessment plays a vital role in urban planning and traffic control, which are influenced by multi-faceted traffic factors involving road infrastructure and traffic flow. Conventional solutions can hardly meet the requirements and expectations of domain experts. In this paper we present a data-driven solution by leveraging a visual analysis system to evaluate the real traffic situations based on taxi trajectory data. A sketch-based visual interface is designed to support dynamic query and visual reasoning of traffic situations within multiple coordinated views. In particular, we propose a novel road-based query model for analysts to interactively conduct evaluation tasks. This model is supported by a bi-directional hash structure, TripHash, which enables real-time responses to the data queries over a huge amount of trajectory data. Case studies with a real taxi GPS trajectory dataset (> 30GB) show that our system performs well for on-demand transport assessment and reasoning. Bao, H. Chen, W. Gu, T. Hong, H. Liang, R. Wang, F. Wang, L. Wu, F. Zhao, Y. coordinated views dynamic query evaluation VAST global positioning system indexes roads topology trajectory visualization 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) hash index road-based query taxi trajectory visual analysis 2014 vast14--7042479 11/12/2014 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) A system for visual analysis of radio signal data. Analysis of radio transmissions is vital for military defense as it provides valuable information about enemy communication and infrastructure. One challenge to the data analysis task is that there are far too many signals for analysts to go through by hand. Even typical signal meta data (such as frequency band, duration, and geographic location) can be overwhelming. In this paper, we present a system for exploring and analyzing such radio signal meta-data. Our system incorporates several visual representations for signal data, designed for readability and ease of comparison, as well as novel algorithms for extracting and classifying consistent signal patterns. We demonstrate the effectiveness of our system using data collected from real missions with an airborne sensor platform. Crnovrsanin, T. Ma, K.-L. Muelder, C. geographic VAST data visualization geospatial analysis repeaters time-frequency analysis visualization wavelet analysis 2014 IEEE Conference on Visual Analytics Science and Technology (VAST) coordinated and multiple views geographic/geospatial visualization intelligence analysis time-varying data 2014 vast15--7347624 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Wavelet-based visualization of time-varying data on graphs. Visualizing time-varying data defined on the nodes of a graph is a challenging problem that has been faced with different approaches. Although techniques based on aggregation, topology, and topic modeling have proven their usefulness, the visual analysis of smooth and/or abrupt data variations as well as the evolution of such variations over time are aspects not properly tackled by existing methods. In this work we propose a novel visualization methodology that relies on graph wavelet theory and stacked graph metaphor to enable the visual analysis of time-varying data defined on the nodes of a graph. The proposed method is able to identify regions where data presents abrupt and mild spacial and/or temporal variation while still been able to show how such changes evolve over time, making the identification of events an easier task. The usefulness of our approach is shown through a set of results using synthetic as well as a real data set involving taxi trips in downtown Manhattan. The methodology was able to reveal interesting phenomena and events such as the identification of specific locations with abrupt variation in the number of taxi pickups. Dias, F. Nonato, L.G. Petronetto, F. Silva, C.T. Valdivia, P. graph theory VAST data visualization public transportation roads visual analytics wavelet analysis wavelet transforms 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) graph wavelets stacked graph visualization time-varying data 2015 vast15--7347636 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Urbane: A 3D framework to support data driven decision making in urban development. Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers. Doraiswamy, H. Ferreira, N. Lage, M. Park, M. Silva, C.T. Vo, H.T. Werner, H. Wilson, L. case study collaboration social VAST buildings cities and towns data visualization decision making stakeholders three-dimensional displays visualization 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) architecture city development gis impact analysis urban data analysis visual analytics 2015 vast15--7347629 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Supporting activity recognition by visual analytics. Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameters is often not clear, making exhaustive evaluations necessary. On this account, we propose a visual analytics approach to supporting users in understanding the complex relationships among parameters, recognized activities, and associated accuracies. First, representative parameter settings are determined. Then, the respective output is computed and statistically analyzed to assess parameters' influence in general. Finally, visualizing the parameter settings along with the activities provides overview and allows to investigate the computed results in detail. Coordinated interaction helps to explore dependencies, compare different settings, and examine individual activities. By integrating automated, visual, and interactive means users can select parameter values that meet desired quality criteria. We demonstrate the application of our solution in a use case with realistic complexity, involving a study of human protagonists in daily living with respect to hundreds of parameter settings. Alsallakh, B. Bogl, M. Kirste, T. Kruger, F. Luboschik, M. Miksch, S. Rohlig, M. Schumann, H. interaction overview security visual analytics VAST algorithm design and analysis data visualization prediction algorithms statistical analysis time series analysis visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) h52 [information interfaces and presentation]: user interfaces???theory and methods i36 [computing methodologies]: computer graphics???methodology and techniques 2015 vast15--7347625 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Mixed-initiative visual analytics using task-driven recommendations. Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support discovery and sensemaking tasks, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Sensemaking researchers have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present candidate design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences about user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach. Bruce, J. Burtner, R. Cook, K.A. Cramer, N. Endert, A. Israel, D. Wolverton, M. insight interaction sensemaking usability visual analytics VAST analytical models computational modeling data models data visualization visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) mixed-initiative visual analytics recommender systems sensemaking task modeling 2015 vast15--7347630 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data. Using transport smart card transaction data to understand the homework dynamics of a city for urban planning is emerging as an alternative to traditional surveys which may be conducted every few years are no longer effective and efficient for the rapidly transforming modern cities. As commuters travel patterns are highly diverse, existing rule-based methods are not fully adequate. In this paper, we present iVizTRANS - a tool which combines an interactive visual analytics (VA) component to aid urban planners to analyse complex travel patterns and decipher activity locations for single public transport commuters. It is coupled with a machine learning component that iteratively learns from the planners classifications to train a classifier. The classifier is then applied to the city-wide smart card data to derive the dynamics for all public transport commuters. Our evaluation shows it outperforms the rule-based methods in previous work. Arunan, A. Huang, Z. Li, G. Li, X. Liang, Y. Min, H. Ng, S. Siong, W. Wu, W. evaluation machine learning visual analytics VAST cities and towns clustering algorithms data visualization feature extraction smart cards spatiotemporal phenomena visualization 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) clustering machine learning origin-destination (od) smart card data spatiotemporal visualization 2015 vast15--7347635 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Interactive visual steering of hierarchical simulation ensembles. Multi-level simulation models, i.e., models where different components are simulated using sub-models of varying levels of complexity, belong to the current state-of-the-art in simulation. The existing analysis practice for multi-level simulation results is to manually compare results from different levels of complexity, amounting to a very tedious and error-prone, trial-and-error exploration process. In this paper, we introduce hierarchical visual steering, a new approach to the exploration and design of complex systems. Hierarchical visual steering makes it possible to explore and analyze hierarchical simulation ensembles at different levels of complexity. At each level, we deal with a dynamic simulation ensemble - the ensemble grows during the exploration process. There is at least one such ensemble per simulation level, resulting in a collection of dynamic ensembles, analyzed simultaneously. The key challenge is to map the multi-dimensional parameter space of one ensemble to the multi-dimensional parameter space of another ensemble (from another level). In order to support the interactive visual analysis of such complex data we propose a novel approach to interactive and semi-automatic parameter space segmentation and comparison. The approach combines a novel interaction technique and automatic, computational methods - clustering, concave hull computation, and concave polygon overlapping - to support the analysts in the cross-ensemble parameter space mapping. In addition to the novel parameter space segmentation we also deploy coordinated multiple views with standard plots. We describe the abstract analysis tasks, identified during a case study, i.e., the design of a variable valve actuation system of a car engine. The study is conducted in cooperation with experts from the automotive industry. Very positive feedback indicates the usefulness and efficiency of the newly proposed approach. Gracanin, D. Hauser, H. Jelovic, M. Matkovic, K. Splechtna, R. case study clustering interaction multiple views VAST aerospace electronics analytical models complex systems computational modeling engines valves visualization 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) interactive visual analysis multi-resolution simulation simulation-ensemble steering 2015 vast15--7347626 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Integrating predictive analytics into a spatiotemporal epidemic simulation. The Epidemic Simulation System (EpiSimS) is a scalable, complex modeling tool for analyzing disease within the United States. Due to its high input dimensionality, time requirements, and resource constraints, simulating over the entire parameter space is unfeasible. One solution is to take a granular sampling of the input space and use simpler predictive models (emulators) in between. The quality of the implemented emulator depends on many factors: robustness, sophistication, configuration, and suitability to the input data. Visual analytics can be leveraged to provide guidance and understanding of these things to the user. In this paper, we have implemented a novel interface and workflow for emulator building and use. We introduce a workflow to build emulators, make predictions, and then analyze the results. Our prediction process first predicts temporal time series, and uses these to derive predicted spatial densities. Integrated into the EpiSimS framework, we target users who are non-experts at statistical modeling. This approach allows for a high level of analysis into the state of the built emulators and their resultant predictions. We present our workflow, models, the associated system, and evaluate the overall utility with feedback from EpiSimS scientists. Bryan, C. Ma, K.-L. Mniszewski, S. Wu, X. time series visual analytics VAST information systems predictive models robustness visual analytics yttrium 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) epidemic visualization predictive modeling spatial-temporal systems visual analytics 2015 vast15--7347633 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) FPSSeer: Visual analysis of game frame rate data. The rate at which frames are rendered in a computer game directly influences both game playability and enjoyability. Players frequently have to deal with the trade-off between high frame rates and good resolution. Analyzing patterns in frame rate data and their correlation with the overall game performance is important in designing games (e.g., graphic card/display setting suggestion and game performance measurement). However, this task is challenging because game frame rates vary both temporally and spatially. In addition, players may adjust their display settings based on their gaming experience and hardware conditions, which further contributes to the unpredictability of frame rates. In this paper, we present a comprehensive visual analytics system FPSSeer, to help game designers gain insight into frame rate data. Our system consists of four major views: 1) a frame rate view to show the overall distribution in a geographic scale, 2) a grid view to show the frame rate distribution and grid element clusters based on their similarity, 3) a FootRiver view to reveal the temporal patterns in game condition changes and potential spatiotemporal correlations, and 4) a comparison view to evaluate game performance discrepancy under different game tests. The real-world case studies demonstrate the effectiveness of our system. The system has been applied to an online commercial game to monitor its performance and to provide feedbacks to designers and developers. Li, Q. Qu, H. Xu, P. geographic hardware insight visual analytics VAST computers correlation data visualization games hardware visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) frame rate data game performance evaluation visual analytics 2015 vast15--7347628 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Four considerations for supporting visual analysis in display ecologies. The current proliferation of large displays and mobile devices presents a number of exciting opportunities for visual analytics and information visualization. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish visual analysis tasks. Based on a comprehensive survey of multi-display systems from a variety of fields, we propose four key considerations for visual analysis in display ecologies: 1) Display Composition, 2) Information Coordination/Transfer, 3) Information Connection, and 4) Display Membership. Different aspects of display ecologies stemming from these design considerations will enable users to transform and empower multiple displays as a display ecology for visual analysis. Chen, J. Chung, H. Joshi, S. North, C. visual analytics VAST collaboration data visualization ecology encoding human computer interaction visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) 2015 vast15--7347637 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) FeatureInsight: Visual support for error-driven feature ideation in text classification. Machine learning requires an effective combination of data, features, and algorithms. While many tools exist for working with machine learning data and algorithms, support for thinking of new features, or feature ideation, remains poor. In this paper, we investigate two general approaches to support feature ideation: visual summaries and sets of errors. We present FeatureInsight, an interactive visual analytics tool for building new dictionary features (semantically related groups of words) for text classification problems. FeatureInsight supports an error-driven feature ideation process and provides interactive visual summaries of sets of misclassified documents. We conducted a controlled experiment evaluating both visual summaries and sets of errors in FeatureInsight. Our results show that visual summaries significantly improve feature ideation, especially in combination with sets of errors. Users preferred visual summaries over viewing raw data, and only preferred examining sets when visual summaries were provided. We discuss extensions of both approaches to data types other than text, and point to areas for future research. Amershi, S. Brooks, M. Drucker, S.M. Kapoor, A. Lee, B. Simard, P. experiment machine learning text visual analytics VAST algorithm design and analysis classification algorithms dictionaries text categorization training data visualization web pages 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) dictionaries features interactive machine learning machine learning text classification visualization 2015 vast15--7347632 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) EgoNetCloud: Event-based egocentric dynamic network visualization. Event-based egocentric dynamic networks are an important class of networks widely seen in many domains. In this paper, we present a visual analytics approach for these networks by combining data-driven network simplifications with a novel visualization design - EgoNetCloud. In particular, an integrated data processing pipeline is proposed to prune, compress and filter the networks into smaller but salient abstractions. To accommodate the simplified network into the visual design, we introduce a constrained graph layout algorithm on the dynamic network. Through a real-life case study as well as conversations with the domain expert, we demonstrate the effectiveness of the EgoNetCloud design and system in completing analysis tasks on event-based dynamic networks. The user study comparing EgoNetCloud with a working system on academic search confirms the effectiveness and convenience of our visual analytics based approach. Hu, Y. Liu, Q. Mu, X. Shi, L. Tang, J. Zhang, Y. case study filter graph graph layout network user study visual analytics VAST clutter collaboration data visualization heuristic algorithms layout stress visualization 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) 2015 vast15--7347631 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) DemographicVis: Analyzing demographic information based on user generated content. The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool. Cho, I. Choo, J. Dou, W. ElTayeby, O. Ribarsky, W. Wang, X. categorical evaluation social text visual analytics VAST data mining feature extraction media pragmatics user-generated content visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) demographic analysis social media user interface visual text analysis 2015 vast15--7347634 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Comparative visual analysis of vector field ensembles. We present a new visual analysis approach to support the comparative exploration of 2D vector-valued ensemble fields. Our approach enables the user to quickly identify the most similar groups of ensemble members, as well as the locations where the variation among the members is high. We further provide means to visualize the main features of the potentially multimodal directional distributions at user-selected locations. For this purpose, directional data is modelled using mixtures of probability density functions (pdfs), which allows us to characterize and classify complex distributions with relatively few parameters. The resulting mixture models are used to determine the degree of similarity between ensemble members, and to construct glyphs showing the direction, spread, and strength of the principal modes of the directional distributions. We also propose several similarity measures, based on which we compute pairwise member similarities in the spatial domain and form clusters of similar members. The hierarchical clustering is shown using dendrograms and similarity matrices, which can be used to select particular members and visualize their variations. A user interface providing multiple linked views enables the simultaneous visualization of aggregated global and detailed local variations, as well as the selection of members for a detailed comparison. Demir, I. Jarema, M. Kehrer, J. Westermann, R. clustering VAST computational modeling data models data visualization uncertainty visualization wind 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) coordinated and multiple views glyph-based techniques uncertainty visualization vector field data 2015 vast15--7347627 11/12/2014 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) Collaborative visual analysis with RCloud. Consider the emerging role of data science teams embedded in larger organizations. Individual analysts work on loosely related problems, and must share their findings with each other and the organization at large, moving results from exploratory data analyses (EDA) into automated visualizations, diagnostics and reports deployed for wider consumption. There are two problems with the current practice. First, there are gaps in this workflow: EDA is performed with one set of tools, and automated reports and deployments with another. Second, these environments often assume a single-developer perspective, while data scientist teams could get much benefit from easier sharing of scripts and data feeds, experiments, annotations, and automated recommendations, which are well beyond what traditional version control systems provide. We contribute and justify the following three requirements for systems built to support current data science teams and users: discoverability, technology transfer, and coexistence. In addition, we contribute the design and implementation of RCloud, a system that supports the requirements of collaborative data analysis, visualization and web deployment. About 100 people used RCloud for two years. We report on interviews with some of these users, and discuss design decisions, tradeoffs and limitations in comparison to other approaches. North, S.C. Scheidegger, C. Urbanek, S. Woodhull, G. VAST collaboration data analysis data visualization electronic mail organizations production visual analytics 2015 IEEE Conference on Visual Analytics Science and Technology (VAST) collaboration computer-supported cooperative work provenance visual analytics process visualization 2015 vast15--2467592 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visually Exploring Transportation Schedules. Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at tripand station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration. Freire, J. Guo, Z. Palomo, C. Silva, C.T. collaboration VAST bandwidth delays kernel public transportation schedules visualization IEEE Transactions on Visualization and Computer Graphics kernel density estimation schedules transportation visual exploration 2015 vast15--2467612 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes. Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator-combined with specialized responsive visualizations-allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example. Albuquerque, G. FoĚ�rster, E. Kreiss, J. LoĚ�we, T. Magnor, M. evaluation time series visual analytics VAST autoregressive processes data models generators parameter estimation time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics order selection time series analysis visual analytics 2015 vast15--2467757 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Visual Analysis and Dissemination of Scientific Literature Collections with SurVis. Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements. Beck, F. Koch, S. Weiskopf, D. clustering database filter overview visual analytics VAST browsers cognition data visualization libraries tag clouds visualization IEEE Transactions on Visualization and Computer Graphics bibliographic data dissemination literature browser visual analytics of documents 2015 vast15--2467555 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VisOHC: Designing Visual Analytics for Online Health Communities. Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables. Choo, J. Chul, B. Huh, J. Kim, S.-H. Lee, S. Yi, J.S. design study network visual analytics VAST atmospheric measurements market research message systems particle measurements prototypes visual analytics IEEE Transactions on Visualization and Computer Graphics conversation analysis design study design study, healthcare healthcare, online health communities thread visualization visual analytics 2015 vast15--2467954 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VEEVVIE: Visual Explorer for Empirical Visualization, VR and Interaction Experiments. Empirical, hypothesis-driven, experimentation is at the heart of the scientific discovery process and has become commonplace in human-factors related fields. To enable the integration of visual analytics in such experiments, we introduce VEEVVIE, the Visual Explorer for Empirical Visualization, VR and Interaction Experiments. VEEVVIE is comprised of a back-end ontology which can model several experimental designs encountered in these fields. This formalization allows VEEVVIE to capture experimental data in a query-able form and makes it accessible through a front-end interface. This front-end offers several multi-dimensional visualization widgets with built-in filtering and highlighting functionality. VEEVVIE is also expandable to support custom experimental measurements and data types through a plug-in visualization widget architecture. We demonstrate VEEVVIE through several case studies of visual analysis, performed on the design and data collected during an experiment on the scalability of high-resolution, immersive, tiled-display walls. Gutenko, I. Kaufman, A. Papadopoulos, C. experiment interaction visual analytics VAST atmospheric measurements data visualization human computer interaction ontologies particle measurements statistical analysis visualization IEEE Transactions on Visualization and Computer Graphics evaluation experiments interaction ontology user studies virtual reality visual analytics visualization 2015 vast15--2467971 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VAiRoma: A Visual Analytics System for Making Sense of Places, Times, and Events in Roman History. Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents. Cho, I. Dou, W. Ribarsky, W. Sauda, E. Wang, D.X. history text user study visual analytics VAST electronic publishing encyclopedias history internet visual analytics IEEE Transactions on Visualization and Computer Graphics text analytics visual analytics wikipedia 2015 vast15--2467871 11/12/2014 IEEE Transactions on Visualization and Computer Graphics VA2: A Visual Analytics Approach for Evaluating Visual Analytics Applications. Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications. Blascheck, T. Ertl, T. John, M. Koch, S. Kurzhals, K. evaluation interaction user study visual analytics VAST data visualization gaze tracking navigation protocols synchronization visual analytics IEEE Transactions on Visualization and Computer Graphics eye tracking interaction logs qualitative evaluation thinking aloud time series data visual analytics 2015 vast15--2467771 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data. We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis. Huang, X. Ma, C. Yang, J. Ye, X. Zhang, C. Zhao, Y. case study coordinated views graph network user study visual analytics VAST cities and towns public transportation roads trajectory visual analytics IEEE Transactions on Visualization and Computer Graphics centrality graph based visual analytics taxi trajectories transportation assessment urban network 2015 vast15--2467531 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text. We present TimeLineCurator, a browser-based authoring tool that automatically extracts event data from temporal references in unstructured text documents using natural language processing and encodes them along a visual timeline. Our goal is to facilitate the timeline creation process for journalists and others who tell temporal stories online. Current solutions involve manually extracting and formatting event data from source documents, a process that tends to be tedious and error prone. With TimeLineCurator, a prospective timeline author can quickly identify the extent of time encompassed by a document, as well as the distribution of events occurring along this timeline. Authors can speculatively browse possible documents to quickly determine whether they are appropriate sources of timeline material. TimeLineCurator provides controls for curating and editing events on a timeline, the ability to combine timelines from multiple source documents, and export curated timelines for online deployment. We evaluate TimeLineCurator through a benchmark comparison of entity extraction error against a manual timeline curation process, a preliminary evaluation of the user experience of timeline authoring, a brief qualitative analysis of its visual output, and a discussion of prospective use cases suggested by members of the target author communities following its deployment. Brehmer, M. Fulda, J. Munzner, T. document evaluation text VAST context data mining data visualization manuals natural language processing pipelines visualization IEEE Transactions on Visualization and Computer Graphics authoring environment journalism system time-oriented data timelines 2015 vast15--2467931 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Visual Causality Analyst: An Interactive Interface for Causal Reasoning. Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst - a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets. Mueller, K. Wang, J. categorical graph network VAST correlation inference algorithms layout linear regression optimization visualization IEEE Transactions on Visualization and Computer Graphics causality high-dimensional data hypothesis testing visual evidence visual knowledge discovery 2015 vast15--2467591 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Role of Uncertainty, Awareness, and Trust in Visual Analytics. Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making. Chul, B. Ellis, G. Keim, D.A. Sacha, D. Senaratne, H. awareness uncertainty visual analytics VAST analytical models buildings data models data visualization uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics human factors knowledge generation trust building uncertainty measures and propagation visual analytics 2015 vast15--2467552 11/12/2014 IEEE Transactions on Visualization and Computer Graphics The Data Context Map: Fusing Data and Attributes into a Unified Display. Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem - observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation - the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix - one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map's application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs. Cheng, S. Mueller, K. matrix VAST context correlation data visualization layout measurement optimization symmetric matrices IEEE Transactions on Visualization and Computer Graphics decision make high dimensional data low-dimensional embedding tradeoffs tradeoffs, visual analytics visual analytics, 2015 vast15--2467553 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Temporal MDS Plots for Analysis of Multivariate Data. Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns. Fischer, F. JaĚ�ckle, D. Keim, D.A. Schreck, T. financial network security social time series VAST communication networks correlation data visualization indexes layout security visualization IEEE Transactions on Visualization and Computer Graphics data reduction multidimensional scaling multivariate data time series 2015 vast15--2467618 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Task-Driven Comparison of Topic Models. Topic modeling, a method of statistically extracting thematic content from a large collection of texts, is used for a wide variety of tasks within text analysis. Though there are a growing number of tools and techniques for exploring single models, comparisons between models are generally reduced to a small set of numerical metrics. These metrics may or may not reflect a model's performance on the analyst's intended task, and can therefore be insufficient to diagnose what causes differences between models. In this paper, we explore task-centric topic model comparison, considering how we can both provide detail for a more nuanced understanding of differences and address the wealth of tasks for which topic models are used. We derive comparison tasks from single-model uses of topic models, which predominantly fall into the categories of understanding topics, understanding similarity, and understanding change. Finally, we provide several visualization techniques that facilitate these tasks, including buddy plots, which combine color and position encodings to allow analysts to readily view changes in document similarity. Alexander, E. Gleicher, M. color document metrics text VAST analytical models color computational modeling encoding measurement numerical models visualization IEEE Transactions on Visualization and Computer Graphics text visualization topic modeling 2015 vast15--2467196 11/12/2014 IEEE Transactions on Visualization and Computer Graphics TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems. Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors. Cao, N. Lin, C. Lin, S. Lin, Y. Lu, J. Shi, C. case study evaluation machine learning social VAST context data models data visualization electronic mail feature extraction twitter visualization IEEE Transactions on Visualization and Computer Graphics anomaly detection social media visual analysis 2015 vast15--2467622 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Supporting Iterative Cohort Construction with Visual Temporal Queries. Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers. Krause, J. Perer, A. Stavropoulos, H. database social VAST databases diseases junctions medical diagnostic imaging sociology statistics visualization IEEE Transactions on Visualization and Computer Graphics cohort definition electronic medical records information visualization visual temporal queries 2015 vast15--2467611 11/12/2014 IEEE Transactions on Visualization and Computer Graphics SensePath: Understanding the Sensemaking Process Through Analytic Provenance. Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process. Attfield, S. Fields, B. Nguyen, P.H. Wheat, A. Wong, B.L.W. Xu, K. insight sensemaking visual analytics VAST context encoding human computer interaction manuals visual analytics web pages IEEE Transactions on Visualization and Computer Graphics analytic provenance coding qualitative research sensemaking timeline visualization transcription 2015 vast15--2468078 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration. We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks. Blaas, J. Holten, D. van den Elzen, S. van Wijk, J.J. interaction network visual analytics VAST animation data visualization indexes manganese principal component analysis visual analytics IEEE Transactions on Visualization and Computer Graphics dimensionality reduction dynamic networks exploration 2015 vast15--2467733 11/12/2014 IEEE Transactions on Visualization and Computer Graphics PhenoBlocks: Phenotype Comparison Visualizations. The differential diagnosis of hereditary disorders is a challenging task for clinicians due to the heterogeneity of phenotypes that can be observed in patients. Existing clinical tools are often text-based and do not emphasize consistency, completeness, or granularity of phenotype reporting. This can impede clinical diagnosis and limit their utility to genetics researchers. Herein, we present PhenoBlocks, a novel visual analytics tool that supports the comparison of phenotypes between patients, or between a patient and the hallmark features of a disorder. An informal evaluation of PhenoBlocks with expert clinicians suggested that the visualization effectively guides the process of differential diagnosis and could reinforce the importance of complete, granular phenotypic reporting. Breslav, S. Brudno, M. Chevalier, F. Glueck, M. Hamilton, P. Khan, A. Wigdor, D. evaluation text visual analytics VAST bioinformatics data visualization diseases medical diagnostic imaging ontologies semantics IEEE Transactions on Visualization and Computer Graphics clinical diagnosis differential hierarchy comparison genomics ontology phenomics phenotype 2015 vast15--2468292 11/12/2014 IEEE Transactions on Visualization and Computer Graphics MotionFlow: Visual Abstraction and Aggregation of Sequential Patterns in Human Motion Tracking Data. Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge. Elmqvist, N. Jang, S. Ramani, K. clustering interaction overview perception usability user study visual analytics VAST context data visualization layout pattern analysis three-dimensional displays tracking visualization IEEE Transactions on Visualization and Computer Graphics expert reviews human motion visualization interactive clustering motion tracking data user study 2015 vast15--2468111 11/12/2014 IEEE Transactions on Visualization and Computer Graphics MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering. Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population. Andrienko, G. Andrienko, N. Brodkorb, F. Kerren, A. Roskosch, P. von Landesberger, T. clustering graph time series visual analytics VAST aggregates clustering algorithms clutter data visualization geology twitter visualization IEEE Transactions on Visualization and Computer Graphics clustering flows graphs movement data networks spatial aggregation temporal aggregation visual analytics 2015 vast15--2468011 11/12/2014 IEEE Transactions on Visualization and Computer Graphics LiteVis: Integrated Visualization for Simulation-Based Decision Support in Lighting Design. State-of-the-art lighting design is based on physically accurate lighting simulations of scenes such as offices. The simulation results support lighting designers in the creation of lighting configurations, which must meet contradicting customer objectives regarding quality and price while conforming to industry standards. However, current tools for lighting design impede rapid feedback cycles. On the one side, they decouple analysis and simulation specification. On the other side, they lack capabilities for a detailed comparison of multiple configurations. The primary contribution of this paper is a design study of LiteVis, a system for efficient decision support in lighting design. LiteVis tightly integrates global illumination-based lighting simulation, a spatial representation of the scene, and non-spatial visualizations of parameters and result indicators. This enables an efficient iterative cycle of simulation parametrization and analysis. Specifically, a novel visualization supports decision making by ranking simulated lighting configurations with regard to a weight-based prioritization of objectives that considers both spatial and non-spatial characteristics. In the spatial domain, novel concepts support a detailed comparison of illumination scenarios. We demonstrate LiteVis using a real-world use case and report qualitative feedback of lighting designers. This feedback indicates that LiteVis successfully supports lighting designers to achieve key tasks more efficiently and with greater certainty. Gröller, E. Luksch, C. Ortner, T. Piringer, H. SchwaĚ�rzler, M. Sorger, J. design study VAST computational modeling decision making industries lighting standards three-dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics coordinated and multiple views integrating spatial and non-spatial data visualization visual knowledge discovery visualization in physical sciences and engineering 2015 vast15--2467615 11/12/2014 IEEE Transactions on Visualization and Computer Graphics InterAxis: Steering Scatterplot Axes via Observation-Level Interaction. Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes, from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios. Choo, J. Endert, A. Kim, H. Park, H. dimension reduction high-dimensional data interaction scatterplot usability visual analytics VAST data models data visualization principal component analysis scalability semantics visual analytics IEEE Transactions on Visualization and Computer Graphics model steering scatterplots user interaction 2015 vast15--2467620 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Interactive Visual Profiling of Musicians. Determining similar objects based upon the features of an object of interest is a common task for visual analytics systems. This process is called profiling, if the object of interest is a person with individual attributes. The profiling of musicians similar to a musician of interest with the aid of visual means became an interesting research question for musicologists working with the Bavarian Musicians Encyclopedia Online. This paper illustrates the development of a visual analytics profiling system that is used to address such research questions. Taking musicological knowledge into account, we outline various steps of our collaborative digital humanities project, priority (1) the definition of various measures to determine the similarity of musicians' attributes, and (2) the design of an interactive profiling system that supports musicologists in iteratively determining similar musicians. The utility of the profiling system is emphasized by various usage scenarios illustrating current research questions in musicology. Focht, J. JaĚ�nicke, S. Scheuermann, G. visual analytics VAST data visualization databases music social network services uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics digital humanities musicians database visualization musicology profiling system visual analytics 2015 vast15--2467619 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data. Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns. Chen, S. Guo, C. Liang, J. Wang, Z. Yuan, X. Zhang, J. Zhang, X. filter social text uncertainty visual analytics VAST data mining data models media reliability semantics transportation uncertainty IEEE Transactions on Visualization and Computer Graphics geo-tagged social media movement sparsely sampling spatial temporal visual analytics uncertainty 2015 vast15--2467991 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Exploring Evolving Media Discourse Through Event Cueing. Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa. Burke, S. Corman, S.R. Davulcu, H. Lu, Y. Maciejewski, R. Montgomery, D. Steptoe, M. Tsai, J. Wang, H. case study insight time series visual analytics VAST analytical models context lenses media meteorology time series analysis visual analytics IEEE Transactions on Visualization and Computer Graphics event detection media analysis time series analysis 2015 vast15--2468151 11/12/2014 IEEE Transactions on Visualization and Computer Graphics egoSlider: Visual Analysis of Egocentric Network Evolution. Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks. Huang, G. Pitipornvivat, N. Qu, H. Wu, Y. Yang, S. Zhao, J. coordinated views graph network social user study VAST data analysis data visualization feature extraction measurement microscopy social network services visualization IEEE Transactions on Visualization and Computer Graphics dynamic graph egocentric network glyph-based design network visualization visual analytics 2015 vast15--2467621 11/12/2014 IEEE Transactions on Visualization and Computer Graphics CiteRivers: Visual Analytics of Citation Patterns. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. Han, Q. Heimerl, F. Koch, S. document visual analytics VAST color data mining joining processes market research metadata tag clouds visualization IEEE Transactions on Visualization and Computer Graphics clustering scientific literature streamgraph visual citation analysis visual document analysis 2015 vast15--2467551 11/12/2014 IEEE Transactions on Visualization and Computer Graphics Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes. While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research. Chen, J. Endert, A. Ragan, E.D. Sanyal, J. evaluation history visual analytics VAST cognition data analysis data visualization history organizations visual analytics IEEE Transactions on Visualization and Computer Graphics analytic provenance conceptual model framework provenance visual analytics visualization 2015 vast15--2467813 11/12/2014 IEEE Transactions on Visualization and Computer Graphics BiSet: Semantic Edge Bundling with Biclusters for Sensemaking. Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics. Mi, P. North, C. Ramakrishnan, N. Sun, M. sensemaking text visual analytics VAST cities and towns clutter encoding image edge detection layout semantics visualization IEEE Transactions on Visualization and Computer Graphics bicluster coordinated relationship semantic edge bundling 2015 vast15--2467554 11/12/2014 IEEE Transactions on Visualization and Computer Graphics An Uncertainty-Aware Approach for Exploratory Microblog Retrieval. Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data. Liao, Q. Liu, M. Liu, S. Pan, S. Wei, F. Zhu, X. evaluation glyph graph uncertainty visual analytics VAST data models data visualization monte carlo methods tagging twitter uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics microblog data mutual reinforcement model uncertainty modeling uncertainty propagation uncertainty visualization 2015 vast15--2467613 11/12/2014 IEEE Transactions on Visualization and Computer Graphics A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights. We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors. Gomez, S.R. Guo, H. Laidlaw, D.H. Ziemkiewicz, C. case study experiment insight interaction metrics user study visual analytics VAST correlation encoding history measurement statistical analysis visual analytics IEEE Transactions on Visualization and Computer Graphics evaluation insight-based evaluation intelligence analysis interaction visual analytics 2015 vast15--2468291 11/12/2014 IEEE Transactions on Visualization and Computer Graphics 3D Regression Heat Map Analysis of Population Study Data. Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease. Glasser, S. Hegenscheid, K. Klemm, P. Lawonn, K. Niemann, U. Preim, B. VoĚ�lzke, H. data mining overview VAST correlation data visualization diseases feature extraction heating measurement three-dimensional displays IEEE Transactions on Visualization and Computer Graphics breast cancer epidemiology heat map hepatic steatonis hepatic steatosis interactive visual analysis regression analysis 2015 vast16--7883520 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual analysis and coding of data-rich user behavior. Investigating user behavior involves abstracting low-level events to higher-level concepts. This requires an analyst to study individual user activities, assign codes which categorize behavior, and develop a consistent classification scheme. To better support this reasoning process of an analyst, we suggest a novel visual analytics approach which integrates rich user data including transcripts, videos, eye movement data, and interaction logs. Word-sized visualizations embedded into a tabular representation provide a space-efficient and detailed overview of user activities. An analyst assigns codes, grouped into code categories, as part of an interactive process. Filtering and searching helps to select specific activities and focus an analysis. A comparison visualization summarizes results of coding and reveals relationships between codes. Editing features support efficient assignment, refinement, and aggregation of codes. We demonstrate the practical applicability and usefulness of our approach in a case study and describe expert feedback. Baltes, S. Beck, F. Blascheck, T. Ertl, T. Weiskopf, D. case study interaction overview visual analytics VAST cognition data visualization encoding tag clouds visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) 2016 vast16--7883519 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) The semantics of sketch: Flexibility in visual query systems for time series data. Sketching allows analysts to specify complex and free-form patterns of interest. Visual query systems can make use of sketches to locate these patterns of interest in large datasets. However, sketching is ambiguous: the same drawing could represent a multitude of potential queries. In this work, we investigate these ambiguities as they apply to visual query systems for time series data. We define a class of “invariants” - the properties of a time series that the analyst wishes to ignore when performing a sketch-based query. We present the results of a crowd-sourced study, showing that these invariants are key components of how people rate the strength of match between sketch and target. We adapt a number of algorithms for time series matching to support invariants in sketches. Lastly, we present a web-deployed prototype sketch-based visual query system that relies on these invariants. We apply the prototype to data from finance, the digital humanities, and political science. Correll, M. Gleicher, M. time series VAST algorithm design and analysis prototypes semantics shape signal processing algorithms time series analysis visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) 2016 vast16--7883509 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) The DataSpace for HIV vaccine studies. The DataSpace for HIV vaccine studies is a discovery tool available on the web to hundreds of investigators. We designed it to help them better understand activity in the field and explore new ideas latent in completed research. The DataSpace harmonizes immunoassay results and study metadata so that a broader research community can pursue more flexible discovery than the typical centrally planned analyses. Insights from human-centered design and beta evaluation suggest strong potential for visual analytics that may also apply to other efforts in open science. The contribution of this paper is to elucidate key domain challenges and demonstrate an application that addresses them. We made several changes to familiar visualizations to support key tasks such as identifying and filtering to a cohort of interest, making meaningful comparisons of time series data from multiple studies that have different plans, and preserving analytic context when making data transformations and comparisons that would normally exclude some data. Hoover, P. Igra, M. McColgin, D. evaluation time series visual analytics VAST data visualization human immunodeficiency virus immune system software timing vaccines visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) hypothesis forming public health time series visualization vaccines visual knowledge discovery 2016 vast16--7883506 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) Supporting visual exploration for multiple users in large display environments. We present a design space exploration of interaction techniques for supporting multiple collaborators exploring data on a shared large display. Our proposed solution is based on users controlling individual lenses using both explicit gestures as well as proxemics: the spatial relations between people and physical artifacts such as their distance, orientation, and movement. We discuss different design considerations for implicit and explicit interactions through the lens, and evaluate the user experience to find a balance between the implicit and explicit interaction styles. Our findings indicate that users favor implicit interaction through proxemics for navigation and collaboration, but prefer using explicit mid-air gestures to perform actions that are perceived to be direct, such as terminating a lens composition. Based on these results, we propose a hybrid technique utilizing both proxemics and mid-air gestures, along with examples applying this technique to other datasets. Finally, we performed a usability evaluation of the hybrid technique and observed user performance improvements in the presence of both implicit and explicit interaction styles. Amini, F. Badam, S.K. Elmqvist, N. Irani, P.P. collaboration evaluation interaction large display navigation usability VAST aerospace electronics collaboration computers lenses mice navigation visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) collaborative sensemaking gestures large displays orientation position proxemics user study visual exploration 2016 vast16--7883513 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) SocialBrands: Visual analysis of public perceptions of brands on social media. Public perceptions of a brand is critical to its performance. While social media has demonstrated a huge potential to shape public perceptions of brands, existing tools are not intuitive and explanatory for domain users to use as they fail to provide a comprehensive analysis framework for perceptions of brands. In this paper, we present SocialBrands, a novel visual analysis tool for brand managers to understand public perceptions of brands on social media. Social-Brands leverages brand personality framework in marketing literature and social computing approaches to compute the personality of brands from three driving factors (user imagery, employee imagery, and official announcement) on social media, and construct an evidence network explaining the association between brand personality and driving factors. These computational results are then integrated with new interactive visualizations to help brand managers understand personality traits and their driving factors. We demonstrate the usefulness and effectiveness of SocialBrands through a series of user studies with brand managers in an enterprise context. Design lessons are also derived from our studies. Akkiraju, R. Gou, L. Liu, H. Liu, X. Shen, H.-W. Xu, A. network social VAST companies data visualization electronic mail twitter visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) 2016 vast16--7883518 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) Shape grammar extraction for efficient query-by-sketch pattern matching in long time series. Long time-series, involving thousands or even millions of time steps, are common in many application domains but remain very difficult to explore interactively. Often the analytical task in such data is to identify specific patterns, but this is a very complex and computationally difficult problem and so focusing the search in order to only identify interesting patterns is a common solution. We propose an efficient method for exploring user-sketched patterns, incorporating the domain expert's knowledge, in time series data through a shape grammar based approach. The shape grammar is extracted from the time series by considering the data as a combination of basic elementary shapes positioned across different amplitudes. We represent these basic shapes using a ratio value, perform binning on ratio values and apply a symbolic approximation. Our proposed method for pattern matching is amplitude-, scale- and translation-invariant and, since the pattern search and pattern constraint relaxation happen at the symbolic level, is very efficient permitting its use in a real-time/online system. We demonstrate the effectiveness of our method in a case study on stock market data although it is applicable to any numeric time series data. Cooper, M. Johansson, J. Muthumanickam, P.K. Vrotsou, K. case study time series VAST approximation algorithms grammar market research pattern matching search problems shape time series analysis 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) regular expression shape grammar sketching symbolic approximation time series user-queries 2016 vast16--7883515 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) SenseMap: Supporting browser-based online sensemaking through analytic provenance. Sensemaking is described as the process in which people collect, organize and create representations of information, all centered around some problem they need to understand. People often get lost when solving complicated tasks using big datasets over long periods of exploration and analysis. They may forget what they have done, are unaware of where they are in the context of the overall task, and are unsure where to continue. In this paper, we introduce a tool, SenseMap, to address these issues in the context of browser-based online sensemaking. We conducted a semi-structured interview with nine participants to explore their behaviors in online sensemaking with existing browser functionality. A simplified sensemaking model based on Pirolli and Card's model is derived to better represent the behaviors we found: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others. SenseMap automatically captures provenance of user sensemaking actions and provides multi-linked views to visualize the collected information and enable users to curate and communicate their findings. To explore how SenseMap is used, we conducted a user study in a naturalistic work setting with five participants completing the same sensemaking task related to their daily work activities. All participants found the visual representation and interaction of the tool intuitive to use. Three of them engaged with the tool and produced successful outcomes. It helped them to organize information sources, to quickly find and navigate to the sources they wanted, and to effectively communicate their findings. Bardill, A. Herd, K. Nguyen, P.H. Salman, B. Wong, B.L.W. Xu, K. interaction sensemaking user study VAST browsers conferences data visualization history interviews visualization web pages 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) analytic provenance browser-based online sensemaking design study sensemaking visual analytics visualization 2016 vast16--7883516 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) PorosityAnalyzer: Visual analysis and evaluation of segmentation pipelines to determine the porosity in fiber-reinforced polymers. In this paper we present PorosityAnalyzer, a novel tool for detailed evaluation and visual analysis of pore segmentation pipelines to determine the porosity in fiber-reinforced polymers (FRPs). The presented tool consists of two modules: the computation module and the analysis module. The computation module enables a convenient setup and execution of distributed off-line-computations on industrial 3D X-ray computed tomography datasets. It allows the user to assemble individual segmentation pipelines in the form of single pipeline steps, and to specify the parameter ranges as well as the sampling of the parameter-space of each pipeline segment. The result of a single segmentation run consists of the input parameters, the calculated 3D binary-segmentation mask, the resulting porosity value, and other derived results (e.g., segmentation pipeline run-time). The analysis module presents the data at different levels of detail by drill-down filtering in order to determine accurate and robust segmentation pipelines. Overview visualizations allow to initially compare and evaluate the segmentation pipelines. With a scatter plot matrix (SPLOM), the segmentation pipelines are examined in more detail based on their input and output parameters. Individual segmentation-pipeline runs are selected in the SPLOM and visually examined and compared in 2D slice views and 3D renderings by using aggregated segmentation masks and statistical contour renderings. PorosityAnalyzer has been thoroughly evaluated with the help of twelve domain experts. Two case studies demonstrate the applicability of our proposed concepts and visualization techniques, and show that our tool helps domain experts to gain new insights and improve their workflow efficiency. Amirkhanov, A. Gröller, E. Heinzl, C. Kastner, J. Weissenböck, J. evaluation matrix overview VAST analytical models data visualization electronic mail image segmentation pipelines three-dimensional displays visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) 2016 vast16--7883511 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) How ideas flow across multiple social groups. Tracking how correlated ideas flow within and across multiple social groups facilitates the understanding of the transfer of information, opinions, and thoughts on social media. In this paper, we present IdeaFlow, a visual analytics system for analyzing the lead-lag changes within and across pre-defined social groups regarding a specific set of correlated ideas, each of which is described by a set of words. To model idea flows accurately, we develop a random-walk-based correlation model and integrate it with Bayesian conditional cointegration and a tensor-based technique. To convey complex lead-lag relationships over time, IdeaFlow combines the strengths of a bubble tree, a flow map, and a timeline. In particular, we develop a Voronoi-treemap-based bubble tree to help users get an overview of a set of ideas quickly. A correlated-clustering-based layout algorithm is used to simultaneously generate multiple flow maps with less ambiguity. We also introduce a focus+context timeline to explore huge amounts of temporal data at different levels of time granularity. Quantitative evaluation and case studies demonstrate the accuracy and effectiveness of IdeaFlow. Chen, Y. Guo, B. Liu, S. Peng, T. Su, J. Wang, X. Yang, J. clustering evaluation focus+context overview social treemap visual analytics VAST correlation lead social groups social network services time series analysis visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) correlated clustering flow map focus+context idea flow lead-lag 2016 vast16--7883512 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) EventAction: Visual analytics for temporal event sequence recommendation. Recommender systems are being widely used to assist people in making decisions, for example, recommending films to watch or books to buy. Despite its ubiquity, the problem of presenting the recommendations of temporal event sequences has not been studied. We propose EventAction, which to our knowledge, is the first attempt at a prescriptive analytics interface designed to present and explain recommendations of temporal event sequences. EventAction provides a visual analytics approach to (1) identify similar records, (2) explore potential outcomes, (3) review recommended temporal event sequences that might help achieve the users' goals, and (4) interactively assist users as they define a personalized action plan associated with a probability of success. Following the design study framework, we designed and deployed EventAction in the context of student advising and reported on the evaluation with a student review manager and three graduate students. Du, F. Plaisant, C. Shneiderman, B. Spring, N. design study evaluation visual analytics VAST history medical services prototypes recommender systems timing visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) prescriptive analytics recommender systems temporal event sequences visual analytics 2016 vast16--7883517 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction. Aiming at massive participation and open access education, Massive Open Online Courses (MOOCs) have attracted millions of learners over the past few years. However, the high dropout rate of learners is considered to be one of the most crucial factors that may hinder the development of MOOCs. To tackle this problem, statistical models have been developed to predict dropout behavior based on learner activity logs. Although predictive models can foresee the dropout behavior, it is still difficult for users to understand the reasons behind the predicted results and further design interventions to prevent dropout. In addition, with a better understanding of dropout, researchers in the area of predictive modeling in turn can improve the models. In this paper, we introduce DropoutSeer, a visual analytics system which not only helps instructors and education experts understand the reasons for dropout, but also allows researchers to identify crucial features which can further improve the performance of the models. Both the heterogeneous data extracted from three different kinds of learner activity logs (i.e., clickstream, forum posts and assignment records) and the predicted results are visualized in the proposed system. Case studies and expert interviews have been conducted to demonstrate the usefulness and effectiveness of DropoutSeer. Boyer, S. Chen, Q. Chen, Y. Qu, H. Veeramachaneni, K. Zhao, M. education visual analytics VAST data models data visualization education feature extraction predictive models visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) design studies machine learning time series data visualization in education 2016 vast16--7883507 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) DocuCompass: Effective exploration of document landscapes. The creation of interactive visualization to analyze text documents has gained an impressive momentum in recent years. This is not surprising in the light of massive and still increasing amounts of available digitized texts. Websites, social media, news wire, and digital libraries are just few examples of the diverse text sources whose visual analysis and exploration offers new opportunities to effectively mine and manage the information and knowledge hidden within them. A popular visualization method for large text collections is to represent each document by a glyph in 2D space. These landscapes can be the result of optimizing pairwise distances in 2D to represent document similarities, or they are provided directly as meta data, such as geo-locations. For well-defined information needs, suitable interaction methods are available for these spatializations. However, free exploration and navigation on a level of abstraction between a labeled document spatialization and reading single documents is largely unsupported. As a result, vital foraging steps for task-tailored actions, such as selecting subgroups of documents for detailed inspection, or subsequent sense-making steps are hampered. To fill in this gap, we propose DocuCompass, a focus+context approach based on the lens metaphor. It comprises multiple methods to characterize local groups of documents, and to efficiently guide exploration based on users' requirements. DocuCompass thus allows for effective interactive exploration of document landscapes without disrupting the mental map of users by changing the layout itself. We discuss the suitability of multiple navigation and characterization methods for different spatializations and texts. Finally, we provide insights generated through user feedback and discuss the effectiveness of our approach. Ertl, T. Han, Q. Heimerl, F. John, M. Koch, S. document focus+context glyph interaction navigation social text VAST layout lenses metadata navigation text analysis two dimensional displays visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) document visualization focus+context interaction techniques text mining visual analytics 2016 vast16--7883514 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) DimScanner: A relation-based visual exploration approach towards data dimension inspection. Exploring multi-dimensional datasets can be cumbersome if data analysts have little knowledge about the data. Various dimension relation inspection tools and dimension exploration tools have been proposed for efficient data examining and understanding. However, the needed workload varies largely with respect to data complexity and user expertise, which can only be reduced with rich background knowledge over the data. In this paper we address the workload challenge with a data structuring and exploration scheme that affords dimension relation detection and that serves as the background knowledge for further investigation. We contribute a novel data structuring scheme that leverages an information-theoretic view structuring algorithm to uncover information-aware relations among different data views, and thereby discloses redundancy and other relation patterns among dimensions. The integrated system, DimScanner, empowers analysts with rich user controls and assistance widgets to interactively detect the relations of multi-dimensional data. Chen, W. Ebertk, D.S. Hou, Y. Hu, W. Huang, X. Xia, J. VAST correlation data visualization inspection layout measurement two dimensional displays visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) data exploration high-dimensional data visualization information-aware relation 2016 vast16--7883510 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) D-Map: Visual analysis of ego-centric information diffusion patterns in social media. Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified. Cao, N. Chen, S. Liang, J. Wang, Z. Wu, Y. Yuan, X. interaction social VAST clutter data visualization diffusion processes radar twitter visualization 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) information diffusion map social media 2016 vast16--7883508 10/25/2016 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) C2A: Crowd consensus analytics for virtual colonoscopy. We present a medical crowdsourcing visual analytics platform called C2A to visualize, classify and filter crowdsourced clinical data. More specifically, C2A is used to build consensus on a clinical diagnosis by visualizing crowd responses and filtering out anomalous activity. Crowdsourcing medical applications have recently shown promise where the non-expert users (the crowd) were able to achieve accuracy similar to the medical experts. This has the potential to reduce interpretation/reading time and possibly improve accuracy by building a consensus on the findings beforehand and letting the medical experts make the final diagnosis. In this paper, we focus on a virtual colonoscopy (VC) application with the clinical technicians as our target users, and the radiologists acting as consultants and classifying segments as benign or malignant. In particular, C2A is used to analyze and explore crowd responses on video segments, created from fly-throughs in the virtual colon. C2A provides several interactive visualization components to build crowd consensus on video segments, to detect anomalies in the crowd data and in the VC video segments, and finally, to improve the non-expert user's work quality and performance by A/B testing for the optimal crowdsourcing platform and application-specific parameters. Case studies and domain experts feedback demonstrate the effectiveness of our framework in improving workers' output quality, the potential to reduce the radiologists' interpretation time, and hence, the potential to improve the traditional clinical workflow by marking the majority of the video segments as benign based on the crowd consensus. Kaufman, A. Mirhosseini, S. Nadeem, S. Park, J.H. filter visual analytics VAST colon crowdsourcing hospitals medical diagnostic imaging virtual colonoscopy visual analytics 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) biomedical applications crowdsourcing virtual colonoscopy visual analytics 2016 vast16--2598545 10/25/2016 IEEE Transactions on Visualization and Computer Graphics What do Constraint Programming Users Want to See? Exploring the Role of Visualisation in Profiling of Models and Search. Constraint programming allows difficult combinatorial problems to be modelled declaratively and solved automatically. Advances in solver technologies over recent years have allowed the successful use of constraint programming in many application areas. However, when a particular solver's search for a solution takes too long, the complexity of the constraint program execution hinders the programmer's ability to profile that search and understand how it relates to their model. Therefore, effective tools to support such profiling and allow users of constraint programming technologies to refine their model or experiment with different search parameters are essential. This paper details the first user-centred design process for visual profiling tools in this domain. We report on: our insights and opportunities identified through an on-line questionnaire and a creativity workshop with domain experts carried out to elicit requirements for analytical and visual profiling techniques; our designs and functional prototypes realising such techniques; and case studies demonstrating how these techniques shed light on the behaviour of the solvers in practice. de la Banda, M.G. Dwyer, T. Goodwin, S. Mears, C. Tack, G. Wallace, M. experiment VAST computational modeling conferences creativity programming prototypes search problems visualization IEEE Transactions on Visualization and Computer Graphics constraint programming profiling tree visualisations user-centred design visual analytics 2016 vast16--2598838 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visualizing the Hidden Activity of Artificial Neural Networks. In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles. Fadel, S.G. FalcĂŁo, A.X. Rauber, P.E. Telea, A.C. machine learning network VAST benchmark testing computational modeling data visualization neural networks neurons training visualization IEEE Transactions on Visualization and Computer Graphics algorithm understanding artificial neural networks dimensionality reduction 2016 vast16--2598466 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visualizing Dimension Coverage to Support Exploratory Analysis. Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e., visually examined for patterns, trends, outliers etc.) and which combinations have not. Yet this information is critical to help the analyst formulate new questions that they have not already answered. We observe that for tabular data, questions are typically comprised of varying combinations of data dimensions (e.g., what are the trends of Sales and Profit for different Regions?). We propose representing analysis history from the angle of dimension coverage (i.e., which data dimensions have been investigated and in which combinations). We use scented widgets to incorporate dimension coverage of the analysts' past work into interaction widgets of a visualization tool. We demonstrate how this approach can assist analysts with the question formation process. Our approach extends the concept of scented widgets to reveal aspects of one's own analysis history, and offers a different perspective on one's past work than typical visualization history tools. Results of our empirical study showed that participants with access to embedded dimension coverage information relied on this information when formulating questions, asked more questions about the data, generated more top-level findings, and showed greater breadth of their analysis without sacrificing depth. Mahyar, N. Sarvghad, A. Tory, M. history interaction VAST data analysis data visualization history market research navigation prototypes visualization IEEE Transactions on Visualization and Computer Graphics dimension coverage empirical laboratory study exploratory data analysis history scented widgets tabular data 2016 vast16--2598495 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis. Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities. Keim, D.A. Lee, J.A. North, S.C. Peltonen, J. Sacha, D. Sedlmair, M. Weiskopf, D. Zhang, L. evaluation interaction machine learning visual analytics VAST algorithm design and analysis analytical models data models data visualization machine learning algorithms manuals visualization IEEE Transactions on Visualization and Computer Graphics dimensionality reduction interactive visualization machine learning visual analytics 2016 vast16--2598695 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visual Analytics for Mobile Eye Tracking. The analysis of eye tracking data often requires the annotation of areas of interest (AOIs) to derive semantic interpretations of human viewing behavior during experiments. This annotation is typically the most time-consuming step of the analysis process. Especially for data from wearable eye tracking glasses, every independently recorded video has to be annotated individually and corresponding AOIs between videos have to be identified. We provide a novel visual analytics approach to ease this annotation process by image-based, automatic clustering of eye tracking data integrated in an interactive labeling and analysis system. The annotation and analysis are tightly coupled by multiple linked views that allow for a direct interpretation of the labeled data in the context of the recorded video stimuli. The components of our analytics environment were developed with a user-centered design approach in close cooperation with an eye tracking expert. We demonstrate our approach with eye tracking data from a real experiment and compare it to an analysis of the data by manual annotation of dynamic AOIs. Furthermore, we conducted an expert user study with 6 external eye tracking researchers to collect feedback and identify analysis strategies they used while working with our application. Hlawatsch, M. Kurzhals, K. Seeger, C. Weiskopf, D. clustering experiment user study visual analytics VAST data visualization gaze tracking labeling mobile communication videos visual analytics IEEE Transactions on Visualization and Computer Graphics eye tracking video visualization visual analytics 2016 vast16--2598444 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Visual Analysis of MOOC Forums with iForum. Discussion forums of Massive Open Online Courses (MOOC) provide great opportunities for students to interact with instructional staff as well as other students. Exploration of MOOC forum data can offer valuable insights for these staff to enhance the course and prepare the next release. However, it is challenging due to the large, complicated, and heterogeneous nature of relevant datasets, which contain multiple dynamically interacting objects such as users, posts, and threads, each one including multiple attributes. In this paper, we present a design study for developing an interactive visual analytics system, called iForum, that allows for effectively discovering and understanding temporal patterns in MOOC forums. The design study was conducted with three domain experts in an iterative manner over one year, including a MOOC instructor and two official teaching assistants. iForum offers a set of novel visualization designs for presenting the three interleaving aspects of MOOC forums (i.e., posts, users, and threads) at three different scales. To demonstrate the effectiveness and usefulness of iForum, we describe a case study involving field experts, in which they use iForum to investigate real MOOC forum data for a course on JAVA programming. Cui, W. Fu, S. Qu, H. Zhao, J. case study design study visual analytics VAST data visualization instruction sets interviews java message systems visual analytics IEEE Transactions on Visualization and Computer Graphics discussion forum mooc temporal visualization visual analytics 2016 vast16--2599378 10/25/2016 IEEE Transactions on Visualization and Computer Graphics VisMatchmaker: Cooperation of the User and the Computer in Centralized Matching Adjustment. Centralized matching is a ubiquitous resource allocation problem. In a centralized matching problem, each agent has a preference list ranking the other agents and a central planner is responsible for matching the agents manually or with an algorithm. While algorithms can find a matching which optimizes some performance metrics, they are used as a black box and preclude the central planner from applying his domain knowledge to find a matching which aligns better with the user tasks. Furthermore, the existing matching visualization techniques (i.e. bipartite graph and adjacency matrix) fail in helping the central planner understand the differences between matchings. In this paper, we present VisMatchmaker, a visualization system which allows the central planner to explore alternatives to an algorithm-generated matching. We identified three common tasks in the process of matching adjustment: problem detection, matching recommendation and matching evaluation. We classified matching comparison into three levels and designed visualization techniques for them, including the number line view and the stacked graph view. Two types of algorithmic support, namely direct assignment and range search, and their interactive operations are also provided to enable the user to apply his domain knowledge in matching adjustment. Law, P. Qu, H. Wu, W. Zheng, Y. evaluation graph matrix metrics VAST bipartite graph computers encoding measurement processor scheduling resource management visualization IEEE Transactions on Visualization and Computer Graphics centralized matching interaction techniques matching visualization visual analytics 2016 vast16--2598497 10/25/2016 IEEE Transactions on Visualization and Computer Graphics VisFlow - Web-based Visualization Framework for Tabular Data with a Subset Flow Model. Data flow systems allow the user to design a flow diagram that specifies the relations between system components which process, filter or visually present the data. Visualization systems may benefit from user-defined data flows as an analysis typically consists of rendering multiple plots on demand and performing different types of interactive queries across coordinated views. In this paper, we propose VisFlow, a web-based visualization framework for tabular data that employs a specific type of data flow model called the subset flow model. VisFlow focuses on interactive queries within the data flow, overcoming the limitation of interactivity from past computational data flow systems. In particular, VisFlow applies embedded visualizations and supports interactive selections, brushing and linking within a visualization-oriented data flow. The model requires all data transmitted by the flow to be a data item subset (i.e. groups of table rows) of some original input table, so that rendering properties can be assigned to the subset unambiguously for tracking and comparison. VisFlow features the analysis flexibility of a flow diagram, and at the same time reduces the diagram complexity and improves usability. We demonstrate the capability of VisFlow on two case studies with domain experts on real-world datasets showing that VisFlow is capable of accomplishing a considerable set of visualization and analysis tasks. The VisFlow system is available as open source on GitHub. Silva, C.T. Yu, B. brushing coordinated views filter usability VAST computational modeling data analysis data models data visualization joining processes pipelines rendering (computer graphics) IEEE Transactions on Visualization and Computer Graphics data flow subset flow model tabular data visualization framework 2016 vast16--2598664 10/25/2016 IEEE Transactions on Visualization and Computer Graphics ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories. Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories. Chen, W. Mei, H. Ren, L. Xu, P. graph interaction visual analytics VAST data visualization industries manufacturing processes real-time systems visual analytics IEEE Transactions on Visualization and Computer Graphics connected industry industry 4.0 manufacturing marey's graph smart factory temporal data visual analytics 2016 vast16--2598831 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Towards Better Analysis of Deep Convolutional Neural Networks. Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable. Li, C. Li, Z. Liu, M. Liu, S. Shi, J. Zhu, J.J.H. cluster graph matrix visual analytics VAST clustering algorithms image edge detection neural networks neurons training visual analytics IEEE Transactions on Visualization and Computer Graphics biclustering deep convolutional neural networks edge bundling matrix reordering rectangle packing 2016 vast16--2598460 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Toward Theoretical Techniques for Measuring the Use of Human Effort in Visual Analytic Systems. Visual analytic systems have long relied on user studies and standard datasets to demonstrate advances to the state of the art, as well as to illustrate the efficiency of solutions to domain-specific challenges. This approach has enabled some important comparisons between systems, but unfortunately the narrow scope required to facilitate these comparisons has prevented many of these lessons from being generalized to new areas. At the same time, advanced visual analytic systems have made increasing use of human-machine collaboration to solve problems not tractable by machine computation alone. To continue to make progress in modeling user tasks in these hybrid visual analytic systems, we must strive to gain insight into what makes certain tasks more complex than others. This will require the development of mechanisms for describing the balance to be struck between machine and human strengths with respect to analytical tasks and workload. In this paper, we argue for the necessity of theoretical tools for reasoning about such balance in visual analytic systems and demonstrate the utility of the Human Oracle Model for this purpose in the context of sensemaking in visual analytics. Additionally, we make use of the Human Oracle Model to guide the development of a new system through a case study in the domain of cybersecurity. Cook, K.A. Crouser, R.J. Endert, A. Franklin, L. case study collaboration insight sensemaking visual analytics VAST animals bioinformatics biological cells education genomics vegetation visualization IEEE Transactions on Visualization and Computer Graphics human oracle mixed initiative systems semantic interaction sensemaking theoretical models visual analytics 2016 vast16--2598445 10/25/2016 IEEE Transactions on Visualization and Computer Graphics TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections. Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets. Choo, J. Elmqvist, N. Kang, K. Kim, M. Park, D. document interaction matrix visual analytics VAST analytical models computational modeling lenses real-time systems two dimensional displays visual analytics IEEE Transactions on Visualization and Computer Graphics magic lens nonnegative matrix factorization t-distributed stochastic neighbor embedding text analytics topic modeling 2016 vast16--2598828 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers. Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclass classification problems. Squares supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data. Our controlled study shows that practitioners can assess performance significantly faster and more accurately with Squares than a confusion matrix, a common performance analysis tool in machine learning. Amershi, S. Lee, B. Ren, D. Suh, J. Williams, J.D. machine learning matrix metrics VAST analytical models data models data visualization debugging measurement performance analysis visualization IEEE Transactions on Visualization and Computer Graphics classification performance analysis usable machine learning 2016 vast16--2598432 10/25/2016 IEEE Transactions on Visualization and Computer Graphics SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations. The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data. Bao, J. Li, Y. Liu, D. Qu, H. Weng, D. Wu, Y. Zheng, Y. data mining visual analytics VAST advertising data mining data visualization public transportation trajectory visual analytics IEEE Transactions on Visualization and Computer Graphics comparative analysis optimal billboard locations taxi trajectory visual analytics 2016 vast16--2598416 10/25/2016 IEEE Transactions on Visualization and Computer Graphics SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories. Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as “What were the taxi trips starting from Main Street and ending at Wall Street in the morning?” or “Where are the taxis arriving at the Art Museum at noon typically coming from?”, tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as “Main Street”, “Wall Street”, and “Art Museum”. In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system. Al-Dohuki, S. Chen, W. Kamw, F. Li, X. Ma, C. Wang, F. Wu, Y. Yang, J. Ye, X. Zhao, Y. geospatial text usability user study VAST data visualization global positioning system public transportation search engines semantics trajectory urban areas IEEE Transactions on Visualization and Computer Graphics name query semantic interaction taxi document taxi trajectories text search engine textualization 2016 vast16--2598469 10/25/2016 IEEE Transactions on Visualization and Computer Graphics PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations. Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design. Brudno, M. Chevalier, F. Glueck, M. Gvozdik, A. Khan, A. Wigdor, D. evaluation hierarchy visual analytics VAST data visualization diseases genomics interviews ontologies visualization IEEE Transactions on Visualization and Computer Graphics cross-sectional cohort analysis human phenotype ontology (hpo) phenotypes 2016 vast16--2598797 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths. Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback. Dontcheva, M. Hoffman, M. Liu, Z. Walker, S. Wang, Y. Wilson, A. VAST companies data mining data visualization interviews navigation visual analytics IEEE Transactions on Visualization and Computer Graphics clickstream data event sequences sequence mining visual analytics 2016 vast16--2598465 10/25/2016 IEEE Transactions on Visualization and Computer Graphics NameClarifier: A Visual Analytics System for Author Name Disambiguation. In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier. Cui, W. Qu, H. Shen, Q. Wu, T. Wu, Y. Yang, H. data mining visual analytics VAST algorithm design and analysis libraries metadata uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics analytical reasoning name disambiguation 2016 vast16--2598830 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science. Lin, G. Liu, X. Shen, H.-W. Wang, J. geographic overview parallel coordinates visual analytics VAST atmospheric modeling computational modeling correlation data visualization meteorology spatial resolution visualization IEEE Transactions on Visualization and Computer Graphics multi-resolution climate ensembles parallel coordinates plots parameter analysis 2016 vast16--2598467 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration. In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks. Bach, B. Behrisch, M. Delz, M. Fekete, J.-D. Hund, M. RĂĽden, L.V. Schreck, T. matrix network VAST data analysis data visualization density measurement feature extraction layout symmetric matrices visualization IEEE Transactions on Visualization and Computer Graphics feature detection/selection matrix visualization quality metrics relational data visual quality measures 2016 vast16--2598796 10/25/2016 IEEE Transactions on Visualization and Computer Graphics GazeDx: Interactive Visual Analytics Framework for Comparative Gaze Analysis with Volumetric Medical Images. We present an interactive visual analytics framework, GazeDx (abbr. of GazeDiagnosis), for the comparative analysis of gaze data from multiple readers examining volumetric images while integrating important contextual information with the gaze data. Gaze pattern comparison is essential to understanding how radiologists examine medical images, and to identifying factors influencing the examination. Most prior work depended upon comparisons with manually juxtaposed static images of gaze tracking results. Comparative gaze analysis with volumetric images is more challenging due to the additional cognitive load on 3D perception. A recent study proposed a visualization design based on direct volume rendering (DVR) for visualizing gaze patterns in volumetric images; however, effective and comprehensive gaze pattern comparison is still challenging due to a lack of interactive visualization tools for comparative gaze analysis. We take the challenge with GazeDx while integrating crucial contextual information such as pupil size and windowing into the analysis process for more in-depth and ecologically valid findings. Among the interactive visualization components in GazeDx, a context-embedded interactive scatterplot is especially designed to help users examine abstract gaze data in diverse contexts by embedding medical imaging representations well known to radiologists in it. We present the results from two case studies with two experienced radiologists, where they compared the gaze patterns of 14 radiologists reading two patients' volumetric CT images. Kim, B. Kim, T.J. Lee, J. Lee, K.H. Seo, J. Song, H. perception scatterplot visual analytics VAST data visualization gaze tracking medical diagnostic imaging three-dimensional displays two dimensional displays visualization IEEE Transactions on Visualization and Computer Graphics context-embedded interactive scatterplot eye tracking gaze pattern comparison gaze visualization interactive temporal chart volumetric medical images 2016 vast16--2598544 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Familiarity Vs Trust: A Comparative Study of Domain Scientists' Trust in Visual Analytics and Conventional Analysis Methods. Combining interactive visualization with automated analytical methods like statistics and data mining facilitates data-driven discovery. These visual analytic methods are beginning to be instantiated within mixed-initiative systems, where humans and machines collaboratively influence evidence-gathering and decision-making. But an open research question is that, when domain experts analyze their data, can they completely trust the outputs and operations on the machine-side? Visualization potentially leads to a transparent analysis process, but do domain experts always trust what they see? To address these questions, we present results from the design and evaluation of a mixed-initiative, visual analytics system for biologists, focusing on analyzing the relationships between familiarity of an analysis medium and domain experts' trust. We propose a trust-augmented design of the visual analytics system, that explicitly takes into account domain-specific tasks, conventions, and preferences. For evaluating the system, we present the results of a controlled user study with 34 biologists where we compare the variation of the level of trust across conventional and visual analytic mediums and explore the influence of familiarity and task complexity on trust. We find that despite being unfamiliar with a visual analytic medium, scientists seem to have an average level of trust that is comparable with the same in conventional analysis medium. In fact, for complex sense-making tasks, we find that the visual analytic system is able to inspire greater trust than other mediums. We summarize the implications of our findings with directions for future research on trustworthiness of visual analytic systems. Cook, K.A. Cramer, N. Dasgupta, A. Lafrance, R.A. Lee, J. Payne, S. Wilson, R. data mining evaluation statistics user study visual analytics VAST bioinformatics biology data analysis data visualization uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics biological data analysis familiarity transparency trust uncertainty 2016 vast16--2598470 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis. In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data. Balcisoy, S. Hauser, H. Kaya, E. Turkay, C. clustering document high-dimensional data interaction visual analytics VAST algorithm design and analysis computers data analysis data visualization principal component analysis visual analytics IEEE Transactions on Visualization and Computer Graphics high dimensional data iterative refinement progressive analytics visual analytics 2016 vast16--2598468 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Characterizing Guidance in Visual Analytics. Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA. Ceneda, D. Gschwandtner, T. May, T. Miksch, S. Schulz, H. Streit, M. Tominski, C. visual analytics VAST automobiles context context modeling data visualization visual analytics IEEE Transactions on Visualization and Computer Graphics assistance guidance model user support visual analytics 2016 vast16--2598472 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation. Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies. Daianu, M. Liu, Q. Shi, L. Thompson, P. Tong, H. Yang, X. graph hierarchy matrix network visual analytics VAST data visualization diffusion tensor imaging diseases sociology visual analytics IEEE Transactions on Visualization and Computer Graphics brain network hybrid representation visual comparison 2016 vast16--2598446 10/25/2016 IEEE Transactions on Visualization and Computer Graphics AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings. Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes. Choo, J. Endert, A. Kim, H. Kwon, B.C. Park, H. Wall, E. high-dimensional data interaction visual analytics VAST analytical models automobiles computational modeling data models data visualization manifolds visual analytics IEEE Transactions on Visualization and Computer Graphics axis mapping axis visualization human-centered visual analytics interactive model steering sketch 2016 vast16--2598543 10/25/2016 IEEE Transactions on Visualization and Computer Graphics Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations. User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas. Breslav, S. Chevalier, F. Glueck, M. Khan, A. Zhao, J. document experiment graph graph layout sensemaking VAST data analysis data visualization human computer interaction layout manuals semantics visualization IEEE Transactions on Visualization and Computer Graphics exploratory sequential data analysis externalization user-authored annotation graph-based visualization 2016 vast16--2598829 10/25/2016 IEEE Transactions on Visualization and Computer Graphics An Analysis of Machine- and Human-Analytics in Classification. In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the “bag of features” approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics. Chen, M. Kothari, V. Tam, G.K.L. machine learning parallel coordinates theory visual analytics VAST analytical models data models data visualization decision trees information theory videos visual analytics IEEE Transactions on Visualization and Computer Graphics classification decision tree facial expression information theory model visual analytics visualization image 2016 vast16--2598415 10/25/2016 IEEE Transactions on Visualization and Computer Graphics A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games. To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset. Chan, Y.Y. Li, Q. Ma, X. Qu, H. Wang, Y. Wang, Z. Xu, P. usability visual analytics VAST data visualization games gold market research pattern matching visual analytics IEEE Transactions on Visualization and Computer Graphics and game reconstruction game play data visualization visual knowledge discovery visual knowledge representation 2016 vast16--2598479 10/25/2016 IEEE Transactions on Visualization and Computer Graphics A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections. Oftentimes multivariate data are not available as sets of equally multivariate tuples, but only as sets of projections into subspaces spanned by subsets of these attributes. For example, one may find data with five attributes stored in six tables of two attributes each, instead of a single table of five attributes. This prohibits the visualization of these data with standard high-dimensional methods, such as parallel coordinates or MDS, and there is hence the need to reconstruct the full multivariate (joint) distribution from these marginal ones. Most of the existing methods designed for this purpose use an iterative procedure to estimate the joint distribution. With insufficient marginal distributions and domain knowledge, they lead to results whose joint errors can be large. Moreover, enforcing smoothness for regularizations in the joint space is not applicable if the attributes are not numerical but categorical. We propose a visual analytics approach that integrates both anecdotal data and human experts to iteratively narrow down a large set of plausible solutions. The solution space is populated using a Monte Carlo procedure which uniformly samples the solution space. A level-of-detail high dimensional visualization system helps the user understand the patterns and the uncertainties. Constraints that narrow the solution space can then be added by the user interactively during the iterative exploration, and eventually a subset of solutions with narrow uncertainty intervals emerges. Mueller, K. Xie, C. Zhong, W. categorical parallel coordinates uncertainty visual analytics VAST diseases image reconstruction iterative methods two dimensional displays uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics high-dimensional data joint distribution reconstruction multivariate data parallel coordinates solution space 2016 vast16--2598471 10/25/2016 IEEE Transactions on Visualization and Computer Graphics A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process. Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system. Caban, J.J. Dabek, F. evaluation visual analytics VAST analytical models automata computational modeling data models data visualization visual analytics IEEE Transactions on Visualization and Computer Graphics analytic provenance machine learning user interactions visual analytics 2016 vast16--2598447 10/25/2016 IEEE Transactions on Visualization and Computer Graphics TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text. We describe TextTile, a data visualization tool for investigation of datasets and questions that require seamless and flexible analysis of structured data and unstructured text. TextTile is based on real-world data analysis problems gathered through our interaction with a number of domain experts and provides a general purpose solution to such problems. The system integrates a set of operations that can interchangeably be applied to the structured as well as to unstructured text part of the data to generate useful data summaries. Such summaries are then organized in visual tiles in a grid layout to allow their analysis and comparison. We validate TextTile with task analysis, use cases and a user study showing the system can be easily learned and proficiently used to carry out nontrivial tasks. Bertini, E. Felix, C. Pandey, A.V. interaction text user study VAST business collaboration data analysis data visualization keyword search medical services visualization IEEE Transactions on Visualization and Computer Graphics exploratory text analysis knowledge discovery text visualization 2016 vast16--2598463 10/25/2016 IEEE Transactions on Visualization and Computer Graphics AnaFe: Visual Analytics of Image-derived Temporal Features–Focusing on the Spleen. We present a novel visualization framework, AnaFe, targeted at observing changes in the spleen over time through multiple image-derived features. Accurate monitoring of progressive changes is crucial for diseases that result in enlargement of the organ. Our system is comprised of multiple linked views combining visualization of temporal 3D organ data, related measurements, and features. Thus it enables the observation of progression and allows for simultaneous comparison within and between the subjects. AnaFe offers insights into the overall distribution of robustly extracted and reproducible quantitative imaging features and their changes within the population, and also enables detailed analysis of individual cases. It performs similarity comparison of temporal series of one subject to all other series in both sick and healthy groups. We demonstrate our system through two use case scenarios on a population of 189 spleen datasets from 68 subjects with various conditions observed over time. Barish, M.A. Dmitriev, K. Gutenko, I. Kaufman, A. visual analytics VAST data visualization diseases imaging shape visual analytics volume measurement IEEE Transactions on Visualization and Computer Graphics abdominal imaging radiomics spleen temporal feature analysis visual knowledge discovery 2016 vast17--2744419 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data. The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies. Cao, N. Lin, C. Lin, Y. Teng, X. Wen, X. Zhu, Q. evaluation interaction VAST algorithm design and analysis anomaly detection data models data visualization spatiotemporal phenomena tensile stress visualization IEEE Transactions on Visualization and Computer Graphics anomaly detection visual analysis 2017 vast17--2744878 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models. Fritz, D. Krishnan, D. ManĂ©, D. Smilkov, D. ViĂ©gas, F.B. Wattenberg, M. Wexler, J. Wilson, J. Wongsuphasawat, K. cluster design study graph machine learning overview VAST computational modeling layout machine learning neural networks standards tools visualization IEEE Transactions on Visualization and Computer Graphics clustered graph dataflow graph graph visualization neural network 2017 vast17--2745178 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses. In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis. Baumgart, M. Kumpf, A. Rautenhaus, M. Riemer, M. Tost, B. Westermann, R. cluster clustering uncertainty visual analytics VAST data visualization market research robustness uncertainty visualization weather forecasting IEEE Transactions on Visualization and Computer Graphics clustering ensemble visualization meteorology uncertainty visualization 2017 vast17--2744685 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visualizing Big Data Outliers Through Distributed Aggregation. Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-<inline-formula><tex-math notation=\ Wilkinson, L. categorical VAST anomaly detection covariance matrices gaussian distribution robustness sociology standards IEEE Transactions on Visualization and Computer Graphics anomalies outliers 2017 vast17--2744378 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Visual Diagnosis of Tree Boosting Methods. Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms. Liu, J. Liu, S. Wang, X. Wu, J. Xiao, J. Zhu, J.J.H. machine learning matrix overview VAST analytical models boosting decision trees tools training vegetation visualization IEEE Transactions on Visualization and Computer Graphics model analysis temporal confusion matrix tree boosting tree visualization 2017 vast17--2744898 10/03/2017 IEEE Transactions on Visualization and Computer Graphics VIGOR: Interactive Visual Exploration of Graph Query Results. Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors. Chau, D.H. Endert, A. Gates, C. Hohman, F. Navathe, S.B. Pienta, R. Roundy, K. Tamersoy, A. business collaboration coordinated views database graph interaction network security sensemaking visual analytics VAST computer security data mining data visualization database systems logic gates visualization IEEE Transactions on Visualization and Computer Graphics graph querying query result visualization subgraph results 2017 vast17--2744418 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Understanding the Relationship Between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy. The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose “black-box” solver. In practice, however, many problems cannot be solved completely automatically, but require a “human-in-the-loop” to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem. Dwyer, T. Haworth, A. Liu, J. Marriott, K. Millar, J. visual analytics VAST brachytherapy mathematical model optimization planning tools visual analytics IEEE Transactions on Visualization and Computer Graphics interactive optimisation interactive systems and tools prostate brachytherapy visual analytics 2017 vast17--2744686 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores. This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain. Dräger, N. Lehmann, D.J. Sips, M. Unger, A. categorical design study VAST lakes meteorology microscopy prototypes sediments semantics visualization IEEE Transactions on Visualization and Computer Graphics categorical data design study time series data visualization in earth science 2017 vast17--2745158 10/03/2017 IEEE Transactions on Visualization and Computer Graphics TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees. Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees. Linhardt, L. Möller, T. MĂĽhlbacher, T. Piringer, H. case study evaluation overview VAST buildings data models decision trees focusing measurement vegetation visualization IEEE Transactions on Visualization and Computer Graphics classification trees model selection pareto optimality sensitivity analysis visual parameter search 2017 vast17--2745258 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics. Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms. Crandell, I. House, L. Leman, S. North, C. Ramakrishnan, N. Wenskovitch, J. clustering dimension reduction overview visual analytics VAST algorithm design and analysis clustering algorithms data visualization manifolds partitioning algorithms visualization IEEE Transactions on Visualization and Computer Graphics algorithms clustering dimension reduction visual analytics 2017 vast17--2743990 10/03/2017 IEEE Transactions on Visualization and Computer Graphics The Interactive Visualization Gap in Initial Exploratory Data Analysis. Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a “visualization gap” during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user. Batch, A. Elmqvist, N. design study VAST big data data science data visualization interviews tools visualization IEEE Transactions on Visualization and Computer Graphics contextual inquiry data science semi-structured interviews visual analytics visualization 2017 vast17--2745279 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs. During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes. Chevalier, F. Glueck, M. Isenberg, P. Khan, A. Zhao, J. awareness document sensemaking uncertainty user study VAST collaboration data analysis data visualization history tools visual analytics IEEE Transactions on Visualization and Computer Graphics collaboration handoff handover interactive visual analysis sensemaking structured externalizations 2017 vast17--2744805 10/03/2017 IEEE Transactions on Visualization and Computer Graphics SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance. Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself. Asano, Y. Behrisch, M. Bernard, J. Keim, D.A. Kraus, M. Sacha, D. Schreck, T. cluster clustering graph history time series visual analytics VAST algorithm design and analysis clustering algorithms data visualization self-organizing feature maps speech time series analysis visualization IEEE Transactions on Visualization and Computer Graphics guidance interaction quality metrics self-organizing maps time series visual analytics visual cluster analysis 2017 vast17--2744738 10/03/2017 IEEE Transactions on Visualization and Computer Graphics SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data. Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens. Chen, Y. Cui, W. Du, X. Lee, D.L. Qu, H. Wang, Y. Wu, Y. Zhao, X. VAST decision making industries measurement urban areas visual analytics IEEE Transactions on Visualization and Computer Graphics multi-criteria decision making multi-dimensional data skyline query skyline visualization visual analytics 2017 vast17--2745083 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Sequence Synopsis: Optimize Visual Summary of Temporal Event Data. Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback. Chen, Y. Ren, L. Xu, P. clustering overview usability visual analytics VAST algorithm design and analysis data mining data models data visualization noise measurement visual analytics IEEE Transactions on Visualization and Computer Graphics data transformation and representation time series data visual analytics visual knowledge representation 2017 vast17--2745080 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework. Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures. Collins, C. El-Assady, M. Keim, D.A. Sevastjanova, R. Sperrle, F. document text visual analytics VAST adaptation models analytical models computational modeling data models learning (artificial intelligence) visual analytics IEEE Transactions on Visualization and Computer Graphics feature detection and tracking iterative optimization reinforcement learning topic model configuration 2017 vast17--2745078 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Podium: Ranking Data Using Mixed-Initiative Visual Analytics. People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas. Brown, E.T. Chawla, R. Das, S. Endert, A. Kalidindi, B. Wall, E. machine learning perception visual analytics VAST computational modeling data models data visualization prototypes support vector machines visual analytics IEEE Transactions on Visualization and Computer Graphics mixed-initiative visual analytics multi-attribute ranking user interaction 2017 vast17--2745118 10/03/2017 IEEE Transactions on Visualization and Computer Graphics PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes-each with its own temporally evolving prevalence and co-occurrence of phenotypes-without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships. PhenoLines enables one to compare phenotype prevalence within and across disease subtype topics, thus supporting subtype characterization, a task that involves identifying a proposed subtype's dominant phenotypes, ages of effect, and clinical validity. We contribute a data transformation workflow that employs the Human Phenotype Ontology to hierarchically organize phenotypes and aggregate the evolving probabilities produced by topic models. We introduce a novel measure of phenotype relevance that can be used to simplify the resulting topology. The design of PhenoLines was motivated by formative interviews with machine learning and clinical experts. We describe the collaborative design process, distill high-level tasks, and report on initial evaluations with machine learning experts and a medical domain expert. These results suggest that PhenoLines demonstrates promising approaches to support the characterization and optimization of topic models. Brudno, M. Chevalier, F. Doshi-Velez, F. Glueck, M. Khan, A. Naeini, M.P. Wigdor, D. machine learning VAST analytical models biological system modeling data models data visualization diseases tools visualization IEEE Transactions on Visualization and Computer Graphics developmental disorder human phenotype ontology (hpo) phenotypes topic models topology simplification 2017 vast17--2744098 10/03/2017 IEEE Transactions on Visualization and Computer Graphics LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets. Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the <inline-formula><tex-math notation=\ Chen, W. Ma, Y. Tung, A.K.H. Wang, Y. Xia, J. Ye, F. graph VAST analytical models data models data visualization manifolds principal component analysis tools visualization IEEE Transactions on Visualization and Computer Graphics high-dimensional data low-dimensional structure manifold subspace visual exploration 2017 vast17--2744080 10/03/2017 IEEE Transactions on Visualization and Computer Graphics How Do Ancestral Traits Shape Family Trees Over Generations? Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals). Cui, W. Dong, H. Fu, S. Qu, H. Zhao, J. design study pixel social zooming VAST animation scalability sociology statistics tools visual analytics IEEE Transactions on Visualization and Computer Graphics design study multiple tree visualization quantitative social science sankey diagram 2017 vast17--2744843 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization. Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types. Basole, R.C. Endert, A. Park, H. Srinivasan, A. interaction network VAST computational modeling data models data visualization image color analysis joining processes prototypes tools IEEE Transactions on Visualization and Computer Graphics network modeling user interaction visual analytics 2017 vast17--2745320 10/03/2017 IEEE Transactions on Visualization and Computer Graphics EventThread: Visual Summarization and Stage Analysis of Event Sequence Data. Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user. Cao, N. Gotz, D. Guo, S. Xu, K. Zha, H. Zhao, R. VAST algorithm design and analysis automobiles clustering algorithms data visualization hidden markov models semantics visualization IEEE Transactions on Visualization and Computer Graphics data clustering illustrative visualization time series data visual knowledge discovery visual knowledge representation 2017 vast17--2744758 10/03/2017 IEEE Transactions on Visualization and Computer Graphics EVA: Visual Analytics to Identify Fraudulent Events. Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms. Gschwandtner, T. Gstrein, E. Kriglstein, S. Kuntner, J. Leite, R.A. Miksch, S. Pohl, M. data mining financial security visual analytics VAST complexity theory data mining data visualization event detection visual analytics IEEE Transactions on Visualization and Computer Graphics business and finance visualization financial fraud detection time series data visual knowledge discovery 2017 vast17--2745280 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Dynamic Influence Networks for Rule-Based Models. We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. Boutillier, P. Burks, A. Fontana, W. Forbes, A.G. Krivine, J. Lee, K. Li, X. interaction network visual analytics VAST analytical models biological system modeling computational modeling data models data visualization proteins IEEE Transactions on Visualization and Computer Graphics biological data visualization dynamic networks protein-protein interaction networks rule-based modeling 2017 vast17--2744683 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Do Convolutional Neural Networks Learn Class Hierarchy? Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data. Bilal, A. Jourabloo, A. Liu, X. Ren, L. Ye, M. hierarchy VAST data visualization feature extraction image recognition neurons training training data IEEE Transactions on Visualization and Computer Graphics confusion matrix convolutional neural networks deep learning image classification large-scale classification 2017 vast17--2744358 10/03/2017 IEEE Transactions on Visualization and Computer Graphics DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks. Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems. Eisemann, E. Gemert, J.V. Höllt, T. Lelieveldt, B.P.F. Pezzotti, N. Vilanova, A. network visual analytics VAST kernel layout neural networks neurons three-dimensional displays training visual analytics IEEE Transactions on Visualization and Computer Graphics deep neural networks machine learning progressive visual analytics 2017 vast17--2744478 10/03/2017 IEEE Transactions on Visualization and Computer Graphics ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding. Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones. Choo, J. Diakopoulos, N. Elmqvist, N. Kim, S. Lee, J. Park, D. document evaluation text user study visual analytics VAST buildings computational modeling data visualization semantics sentiment analysis text analysis visual analytics IEEE Transactions on Visualization and Computer Graphics concepts text analytics text classification text summarization visual analytics word embedding 2017 vast17--2744818 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study. Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling. Bernard, J. Fellner, D. Hutter, M. Sedlmair, M. Zeppelzauer, M. dimension reduction experiment machine learning visual analytics VAST data models data visualization labeling uncertainty visual analytics IEEE Transactions on Visualization and Computer Graphics active learning classification dimensionality reduction evaluation experiment information visualization labeling machine learning visual analytics visual-interactive labeling 2017 vast17--2745085 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Clustervision: Visual Supervision of Unsupervised Clustering. Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data. Eysenbach, B. De Filippi, C. Kwon, B.C. Ng, K. Perer, A. Stewart, W.F. Verma, J. case study cluster clustering machine learning metrics visual analytics VAST clustering algorithms data visualization indexes measurement partitioning algorithms visual analytics IEEE Transactions on Visualization and Computer Graphics interactive visual clustering quality metrics unsupervised clustering visual analytics 2017 vast17--2744322 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Clustering Trajectories by Relevant Parts for Air Traffic Analysis. Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering. Andrienko, G. Andrienko, N. Fuchs, G. Garcia, J.M.C. cluster clustering VAST algorithm design and analysis clustering algorithms data visualization guidelines three-dimensional displays trajectory visualization IEEE Transactions on Visualization and Computer Graphics air traffic movement data analysis trajectory clustering visual analytics 2017 vast17--2745181 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis. Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. Andrienko, G. Breitkreutz, T. GoldlĂĽcke, B. Grossniklaus, M. Janetzko, H. Keim, D.A. Lamprecht, A. Schreck, T. Stein, M. Zimmermann, P. visual analytics VAST computer vision data visualization feature extraction image color analysis trajectory video recording visualization IEEE Transactions on Visualization and Computer Graphics immersive analytics sport analytics visual analytics 2017 vast17--2744458 10/03/2017 IEEE Transactions on Visualization and Computer Graphics BiDots: Visual Exploration of Weighted Biclusters. Discovering and analyzing biclusters, i.e., two sets of related entities with close relationships, is a critical task in many real-world applications, such as exploring entity co-occurrences in intelligence analysis, and studying gene expression in bio-informatics. While the output of biclustering techniques can offer some initial low-level insights, visual approaches are required on top of that due to the algorithmic output complexity. This paper proposes a visualization technique, called BiDots, that allows analysts to interactively explore biclusters over multiple domains. BiDots overcomes several limitations of existing bicluster visualizations by encoding biclusters in a more compact and cluster-driven manner. A set of handy interactions is incorporated to support flexible analysis of biclustering results. More importantly, BiDots addresses the cases of weighted biclusters, which has been underexploited in the literature. The design of BiDots is grounded by a set of analytical tasks derived from previous work. We demonstrate its usefulness and effectiveness for exploring computed biclusters with an investigative document analysis task, in which suspicious people and activities are identified from a text corpus. Chen, F. Chiu, P. Sun, M. Zhao, J. cluster document intelligence analysis text VAST algorithm design and analysis data mining data visualization gene expression organizations sparse matrices visualization IEEE Transactions on Visualization and Computer Graphics biclustering coordinated relationship analysis visual analytics 2017 vast17--2745180 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Beyond Tasks: An Activity Typology for Visual Analytics. As Visual Analytics (VA) research grows and diversifies to encompass new systems, techniques, and use contexts, gaining a holistic view of analytic practices is becoming ever more challenging. However, such a view is essential for researchers and practitioners seeking to develop systems for broad audiences that span multiple domains. In this paper, we interpret VA research through the lens of Activity Theory (AT) - a framework for modelling human activities that has been influential in the field of Human-Computer Interaction. We first provide an overview of Activity Theory, showing its potential for thinking beyond tasks, representations, and interactions to the broader systems of activity in which interactive tools are embedded and used. Next, we describe how Activity Theory can be used as an organizing framework in the construction of activity typologies, building and expanding upon the tradition of abstract task taxonomies in the field of Information Visualization. We then apply the resulting process to create an activity typology for Visual Analytics, synthesizing a wide range of systems and activity concepts from the literature. Finally, we use this typology as the foundation of an activity-centered design process, highlighting both tensions and opportunities in the design space of VA systems. Edge, D. Larson, J. Henry Riche, N. White, C. interaction overview theory visual analytics VAST data analysis data visualisation human computer interaction interactive systems user centred design IEEE Transactions on Visualization and Computer Graphics activity theory activity-centered design human-computer interaction literature review visual analytics 2017 vast17--2744684 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Applying Pragmatics Principles for Interaction with Visual Analytics. Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools. Dykeman, I. Hoque, E. Setlur, V. Tory, M. interaction visual analytics VAST coherence data visualization natural languages pragmatics tools visual analytics IEEE Transactions on Visualization and Computer Graphics ambiguity feedback interaction language pragmatics natural language visual analytics 2017 vast17--2744938 10/03/2017 IEEE Transactions on Visualization and Computer Graphics Analyzing the Training Processes of Deep Generative Models. Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs. Cao, K. Liu, M. Liu, S. Shi, J. Zhu, J.J.H. machine learning time series visual analytics VAST analytical models neurons time series analysis tools training visual analytics IEEE Transactions on Visualization and Computer Graphics blue noise sampling credit assignment deep generative models deep learning 2017 vast17--2744718 10/03/2017 IEEE Transactions on Visualization and Computer Graphics ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models. Andrews, P.Y. Chau, D.H. Kahng, M. Kalro, A. coordinated views graph machine learning network overview VAST computational modeling data models data visualization facebook machine learning neurons tools IEEE Transactions on Visualization and Computer Graphics deep learning information visualization machine learning visual analytics 2017 vast17--2745139 10/03/2017 IEEE Transactions on Visualization and Computer Graphics A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data. Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach. Chen, W. Chou, J. Guan, H. Lao, T. Ma, K.-L. Wang, X. case study VAST data models data privacy data visualization pipelines privacy syntactics visualization IEEE Transactions on Visualization and Computer Graphics differential privacy privacy preserving visualization syntactic anonymity utility aware anonymization 2017 vast17--8585669 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics. Visual analytic tools combine the complementary strengths of humans and machines in human-in-the-loop systems. Humans provide invaluable domain expertise and sensemaking capabilities to this discourse with analytic models; however, little consideration has yet been given to the ways inherent human biases might shape the visual analytic process. In this paper, we establish a conceptual framework for considering bias assessment through human-in-the-loop systems and lay the theoretical foundations for bias measurement. We propose six preliminary metrics to systematically detect and quantify bias from user interactions and demonstrate how the metrics might be implemented in an existing visual analytic system, InterAxis. We discuss how our proposed metrics could be used by visual analytic systems to mitigate the negative effects of cognitive biases by making users aware of biased processes throughout their analyses. Blaha, L.M. Endert, A. Franklin, L. Wall, E. metrics sensemaking visual analytics VAST analytical models cognition computational modeling data analysis decision making measurement visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) cognitive bias h.5.0 [information systems]: human-computer interaction-general human-in-the-loop mixed initiative user interaction visual analytics 2017 vast17--8585594 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Visualizing Real-Time Strategy Games: The Example of StarCraft II. We present a visualization system for users to examine real-time strategy games, which have become very popular globally in recent years. Unlike previous systems that focus on showing statistics and build order, our system can depict the most important part - battles in the games. Specifically, we visualize detailed movements of armies belonging to respective nations on a map and enable users to examine battles from a global view to a local view. In the global view, battles are depicted by curved arrows revealing how the armies enter and escape from the battlefield. By observing the arrows and the height map, users can make sense of offensive and defensive strategies easily. In the local view, units of each type are rendered on the map, and their movements are represented by animation. We also render an attack line between a pair of units if one of them can attack the other to help users analyze the advantages and disadvantages of a particular formation. Accordingly, users can utilize our system to discover statistics, build order, and battles, and learn strategies from games played by professionals. Chuang, J. Kuan, Y. Wang, Y. animation statistics VAST artificial intelligence buildings games real-time systems tools training visualization 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) game visualization real-time strategy games starcraft trajectories 2017 vast17--8585647 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Visual Causality Analysis Made Practical. Deriving the exact casual model that governs the relations between variables in a multidimensional dataset is difficult in practice. It is because causal inference algorithms by themselves typically cannot encode an adequate amount of domain knowledge to break all ties. Visual analytic approaches are considered a feasible alternative to fully automated methods. However, their application in real-world scenarios can be tedious. This paper focuses on these practical aspects of visual causality analysis. The most imperative of these aspects is posed by Simpson' Paradox. It implies the existence of multiple causal models differing in both structure and parameter depending on how the data is subdivided. We propose a comprehensive interface that engages human experts in identifying these subdivisions and allowing them to establish the corresponding causal models via a rich set of interactive facilities. Other features of our interface include: (1) a new causal network visualization that emphasizes the flow of causal dependencies, (2) a model scoring mechanism with visual hints for interactive model refinement, and (3) flexible approaches for handling heterogeneous data. Various real-world data examples are given. Mueller, K. Wang, J. network VAST analytical models correlation data models data visualization inference algorithms visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) causality high-dimensional data hypothesis testing visual evidence visual knowledge discovery 2017 vast17--8585721 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Understanding Hidden Memories of Recurrent Neural Networks. Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts. Cao, S. Chen, Y. Li, Z. Ming, Y. Qu, H. Song, Y. Zhang, R. cluster clustering glyph usability visual analytics VAST analytical models computer architecture machine learning recurrent neural networks task analysis visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) co-clustering recurrent neural networks understanding neural model visual analytics 2017 vast17--8585487 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) The “y” of it Matters, Even for Storyline Visualization. Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The original definition of a storyline visualization requires data defined in terms of explicit interaction groups. Relaxing this definition allows storyline visualization to be applied more generally, but this creates questions about how the y-coordinate should encode interactions when this is tied to a particular place or state. To answer this question, we conducted a design study where we considered two layout algorithm design alternatives within a geo-temporal analysis tool written to solve part of the VAST Challenge 2014. We measured the performance of users at overview and detail oriented tasks between two storyline layout algorithms. To the best of our knowledge, this paper is the first work to question the design principles for storyline visualization, and what we found surprised us. For overview tasks with the alternative layout, which has a consistent encoding for the y-coordinate, users performed moderately better (p <; .05) than the storyline layout based on existing design constraints and aesthetic criteria. Our empirical findings were also supported by first-hand accounts taken from interviews with multiple expert analysts, who suggested that the inconsistent meaning of the y-axis was misleading. These findings led us to design a new storyline layout algorithm that is a “best of both” where the y-axis has a consistent meaning but aesthetic criteria (e.g., line crossings) are considered. Arendt, D. Pirrung, M. design study interaction overview VAST data visualization heuristic algorithms interviews layout public transportation tools visualization 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) geospatial analysis interaction context layout algorithms storyline visualization vast challenge 2017 vast17--8585498 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics. Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans' tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively. Aigner, W. Amor-AmorĂłs, A. Federico, P. Miksch, S. Rind, A. Wagner, M. theory visual analytics VAST analytical models cognition data analysis data models data visualization knowledge based systems visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) automated analysis explicit knowledge information visualization tacit knowledge theory and model visual analytics 2017 vast17--8585665 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) The Anchoring Effect in Decision-Making with Visual Analytics. Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users' interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias. Cho, I. Dou, W. Karduni, A. Santhanam, S. Shaikh, S. Wesslen, R. categorical interaction multiple views visual analytics VAST analytical models data visualization decision making task analysis uncertainty visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) anchoring effect cognitive bias interaction log analysis sense making visual analytics 2017 vast17--8585733 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations. Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question-answering (QA) systems. Through in-depth studies with QA researchers, we identify tasks and goals of evaluation analysis and derive a set of design rationales, based on which we propose a novel approach termed prismatic analysis. Prismatic analysis examines data through multiple ways of categorization (referred as angles). Categories in each angle are measured by aggregate metrics to enable diverse comparison scenarios. To facilitate prismatic analysis of QA evaluations, we design and implement the Question Space Anglyzer (QSAnglyzer), a visual analytics (VA) tool. In QSAnglyzer, the high-dimensional space formed by questions is divided into categories based on several angles (e.g., topic and question type). Each category is aggregated by accuracy, the number of questions, and accuracy variance across evaluations. QSAnglyzer visualizes these angles so that QA researchers can examine and compare evaluations from various aspects both individually and collectively. Furthermore, QA researchers filter questions based on any angle by clicking to construct complex queries. We validate QSAnglyzer through controlled experiments and by expert reviews. The results indicate that when using QSAnglyzer, users perform analysis tasks faster (p <; 0.01) and more accurately (p <; 0.05), and are quick to gain new insight. We discuss how prismatic analysis and QSAnglyzer scaffold evaluation analysis, and provide directions for future research. Chen, N. Kim, B. evaluation experiment filter insight metrics visual analytics VAST aggregates computational modeling knowledge discovery measurement task analysis visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) h.5.2 [information interfaces and presentation]: user interfaces— interactive visualization multi-experiment analysis prismatic analysis question answering visual analytics visual comparison visual exploration visualization 2017 vast17--8585613 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces. Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions. Behrisch, M. Hund, M. Jäckle, D. Keim, D.A. Schreck, T. high-dimensional data VAST data analysis data visualization machine learning redundancy task analysis visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) h.5.2 [information interfaces and presentation] interaction styles user interfaces—graphical user interfaces 2017 vast17--8585505 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Interactive Visual Alignment of Medieval Text Versions. Textual criticism consists of the identification and analysis of variant readings among different versions of a text. Being a relatively simple task for modern languages, the collation of medieval text traditions ranges from the complex to the virtually impossible depending on the degree of instability of textual transmission. We present a visual analytics environment that supports computationally aligning such complex textual differences typical of orally inflected medieval poetry. For the purpose of analyzing alignment, we provide interactive visualizations for different text hierarchy levels, specifically, a meso reading view to support investigating repetition and variance at the line level across text segments. In addition to outlining important aspects of our interdisciplinary collaboration, we emphasize the utility of the proposed system by various usage scenarios in medieval French literature. Jänicke, S. Wrisley, D.J. collaboration hierarchy text visual analytics VAST data visualization heating systems phonetics prototypes task analysis visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) 2017 vast17--8585638 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media. Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data. Chen, S. Liang, J. Lin, L. Yuan, X. Zhang, X. social visual analytics VAST data visualization rivers twitter urban areas visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) event analysis map-like visual metaphor social media spatial temporal visual analytics 2017 vast17--8585658 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data. Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data. Cho, I. Dou, W. Ribarsky, W. Volkova, S. Wesslen, R. graph sensemaking social text VAST event detection law enforcement time measurement twitter visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) event detection and analysis social media analysis visual analytics 2017 vast17--8585484 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization. We present CRICTO, a new crowdsourcing visual analytics environment for making sense of and analyzing text data, whereby multiple crowdworkers are able to parallelize the simple information schematization tasks of relating and connecting entities across documents. The diverse links from these schematization tasks are then automatically combined and the system visualizes them based on the semantic types of the linkages. CRICTO also includes several tools that allow analysts to interactively explore and refine crowdworkers' results to better support their own sensemaking processes. We evaluated CRICTO's techniques and analysis workflow with deployments of CRICTO using Amazon Mechanical Turk and a user study that assess the effect of crowdsourced schematization in sensemaking tasks. The results of our evaluation show that CRICTO's crowdsourcing approaches and workflow help analysts explore diverse aspects of datasets, and uncover more accurate hidden stories embedded in the text datasets. Andrews, C. Chung, H. Dasari, S.P. Nandhakumar, S. evaluation sensemaking text user study visual analytics VAST collaboration crowdsourcing data visualization task analysis tools visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) crowdsourcing sensemaking visual text analytics 2017 vast17--8585720 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations. Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved. Aphinyanaphongs, Y. Bertini, E. Dasgupta, A. Krause, J. Swartz, J. case study collaboration machine learning statistics visual analytics VAST analytical models collaboration data models machine learning predictive models visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) interpretation machine learning visual analytics 2017 vast17--8585646 10/03/2017 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) A Visual Analytics System for Optimizing Communications in Massively Parallel Applications. Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profiled at a is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for Mini AMR application on 4,096 MPI processes. Fujiwara, T. Ma, K.-L. Malakar, P. Papka, M.E. Reda, K. Vishwanath, V. network visual analytics VAST data visualization network topology routing supercomputers tools two dimensional displays visual analytics 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) communication visualization parallel communications performance analysis supercomputing visual analytics 2017